WorldWideScience

Sample records for fuzzy decision tree

  1. Building of fuzzy decision trees using ID3 algorithm

    Science.gov (United States)

    Begenova, S. B.; Avdeenko, T. V.

    2018-05-01

    Decision trees are widely used in the field of machine learning and artificial intelligence. Such popularity is due to the fact that with the help of decision trees graphic models, text rules can be built and they are easily understood by the final user. Because of the inaccuracy of observations, uncertainties, the data, collected in the environment, often take an unclear form. Therefore, fuzzy decision trees becoming popular in the field of machine learning. This article presents a method that includes the features of the two above-mentioned approaches: a graphical representation of the rules system in the form of a tree and a fuzzy representation of the data. The approach uses such advantages as high comprehensibility of decision trees and the ability to cope with inaccurate and uncertain information in fuzzy representation. The received learning method is suitable for classifying problems with both numerical and symbolic features. In the article, solution illustrations and numerical results are given.

  2. Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

    Science.gov (United States)

    Doubravsky, Karel; Dohnal, Mirko

    2015-01-01

    Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

  3. Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

    Directory of Open Access Journals (Sweden)

    Karel Doubravsky

    Full Text Available Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (rechecked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

  4. Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

    Science.gov (United States)

    Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

    Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

  5. Cardiovascular Dysautonomias Diagnosis Using Crisp and Fuzzy Decision Tree: A Comparative Study.

    Science.gov (United States)

    Kadi, Ilham; Idri, Ali

    2016-01-01

    Decision trees (DTs) are one of the most popular techniques for learning classification systems, especially when it comes to learning from discrete examples. In real world, many data occurred in a fuzzy form. Hence a DT must be able to deal with such fuzzy data. In fact, integrating fuzzy logic when dealing with imprecise and uncertain data allows reducing uncertainty and providing the ability to model fine knowledge details. In this paper, a fuzzy decision tree (FDT) algorithm was applied on a dataset extracted from the ANS (Autonomic Nervous System) unit of the Moroccan university hospital Avicenne. This unit is specialized on performing several dynamic tests to diagnose patients with autonomic disorder and suggest them the appropriate treatment. A set of fuzzy classifiers were generated using FID 3.4. The error rates of the generated FDTs were calculated to measure their performances. Moreover, a comparison between the error rates obtained using crisp and FDTs was carried out and has proved that the results of FDTs were better than those obtained using crisp DTs.

  6. A fuzzy decision tree method for fault classification in the steam generator of a pressurized water reactor

    International Nuclear Information System (INIS)

    Zio, Enrico; Baraldi, Piero; Popescu, Irina Crenguta

    2009-01-01

    This paper extends a method previously introduced by the authors for building a transparent fault classification algorithm by combining the fuzzy clustering, fuzzy logic and decision trees techniques. The baseline method transforms an opaque, fuzzy clustering-based classification model into a fuzzy logic inference model based on linguistic rules which can be represented by a decision tree formalism. The classification model thereby obtained is transparent in that it allows direct interpretation and inspection of the model. An extension in the procedure for the development of the fuzzy logic inference model is introduced to allow the treatment of more complicated cases, e.g. splitted and overlapping clusters. The corresponding computational tool developed relies on a number of parameters which can be tuned by the user to optimally compromise the level of transparency of the classification process and its efficiency. A numerical application is presented with regards to the fault classification in the Steam Generator of a Pressurized Water Reactor.

  7. Fuzzy tree automata and syntactic pattern recognition.

    Science.gov (United States)

    Lee, E T

    1982-04-01

    An approach of representing patterns by trees and processing these trees by fuzzy tree automata is described. Fuzzy tree automata are defined and investigated. The results include that the class of fuzzy root-to-frontier recognizable ¿-trees is closed under intersection, union, and complementation. Thus, the class of fuzzy root-to-frontier recognizable ¿-trees forms a Boolean algebra. Fuzzy tree automata are applied to processing fuzzy tree representation of patterns based on syntactic pattern recognition. The grade of acceptance is defined and investigated. Quantitative measures of ``approximate isosceles triangle,'' ``approximate elongated isosceles triangle,'' ``approximate rectangle,'' and ``approximate cross'' are defined and used in the illustrative examples of this approach. By using these quantitative measures, a house, a house with high roof, and a church are also presented as illustrative examples. In addition, three fuzzy tree automata are constructed which have the capability of processing the fuzzy tree representations of ``fuzzy houses,'' ``houses with high roofs,'' and ``fuzzy churches,'' respectively. The results may have useful applications in pattern recognition, image processing, artificial intelligence, pattern database design and processing, image science, and pictorial information systems.

  8. Model predictive control using fuzzy decision functions

    NARCIS (Netherlands)

    Kaymak, U.; Costa Sousa, da J.M.

    2001-01-01

    Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

  9. Using fuzzy arithmetic in containment event trees

    International Nuclear Information System (INIS)

    Rivera, S.S.; Baron, Jorge H.

    2000-01-01

    The use of fuzzy arithmetic is proposed for the evaluation of containment event trees. Concepts such as improbable, very improbable, and so on, which are subjective by nature, are represented by fuzzy numbers. The quantitative evaluation of containment event trees is based on the extension principle, by which operations on real numbers are extended to operations on fuzzy numbers. Expert knowledge is considered as state of the base variable with a normal distribution, which is considered to represent the membership function. Finally, this paper presents results of an example calculation of a containment event tree for the CAREM-25 nuclear power plant, presently under detailed design stage at Argentina. (author)

  10. Fuzzy Uncertainty Evaluation for Fault Tree Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Ki Beom; Shim, Hyung Jin [Seoul National University, Seoul (Korea, Republic of); Jae, Moo Sung [Hanyang University, Seoul (Korea, Republic of)

    2015-05-15

    This traditional probabilistic approach can calculate relatively accurate results. However it requires a long time because of repetitive computation due to the MC method. In addition, when informative data for statistical analysis are not sufficient or some events are mainly caused by human error, the probabilistic approach may not be possible because uncertainties of these events are difficult to be expressed by probabilistic distributions. In order to reduce the computation time and quantify uncertainties of top events when basic events whose uncertainties are difficult to be expressed by probabilistic distributions exist, the fuzzy uncertainty propagation based on fuzzy set theory can be applied. In this paper, we develop a fuzzy uncertainty propagation code and apply the fault tree of the core damage accident after the large loss of coolant accident (LLOCA). The fuzzy uncertainty propagation code is implemented and tested for the fault tree of the radiation release accident. We apply this code to the fault tree of the core damage accident after the LLOCA in three cases and compare the results with those computed by the probabilistic uncertainty propagation using the MC method. The results obtained by the fuzzy uncertainty propagation can be calculated in relatively short time, covering the results obtained by the probabilistic uncertainty propagation.

  11. The Performance Analysis of the Map-Aided Fuzzy Decision Tree Based on the Pedestrian Dead Reckoning Algorithm in an Indoor Environment

    Directory of Open Access Journals (Sweden)

    Kai-Wei Chiang

    2015-12-01

    Full Text Available Hardware sensors embedded in a smartphone allow the device to become an excellent mobile navigator. A smartphone is ideal for this task because its great international popularity has led to increased phone power and since most of the necessary infrastructure is already in place. However, using a smartphone for indoor pedestrian navigation can be problematic due to the low accuracy of sensors, imprecise predictability of pedestrian motion, and inaccessibility of the Global Navigation Satellite System (GNSS in some indoor environments. Pedestrian Dead Reckoning (PDR is one of the most common technologies used for pedestrian navigation, but in its present form, various errors tend to accumulate. This study introduces a fuzzy decision tree (FDT aided by map information to improve the accuracy and stability of PDR with less dependency on infrastructure. First, the map is quickly surveyed by the Indoor Mobile Mapping System (IMMS. Next, Bluetooth beacons are implemented to enable the initializing of any position. Finally, map-aided FDT can estimate navigation solutions in real time. The experiments were conducted in different fields using a variety of smartphones and users in order to verify stability. The contrast PDR system demonstrates low stability for each case without pre-calibration and post-processing, but the proposed low-complexity FDT algorithm shows good stability and accuracy under the same conditions.

  12. Fuzzy set theoretic approach to fault tree analysis | Tyagi ...

    African Journals Online (AJOL)

    This approach can be widely used to improve the reliability and to reduce the operating cost of a system. The proposed techniques are discussed and illustrated by taking an example of a nuclear power plant. Keywords: Fault tree, Triangular and Trapezoidal fuzzy number, Fuzzy importance, Ranking of fuzzy numbers ...

  13. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

    Science.gov (United States)

    Pradhan, Biswajeet

    2013-02-01

    The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e

  14. FUZZY DECISION MAKING MODEL FOR BYZANTINE AGREEMENT

    Directory of Open Access Journals (Sweden)

    S. MURUGAN

    2014-04-01

    Full Text Available Byzantine fault tolerance is of high importance in the distributed computing environment where malicious attacks and software errors are common. A Byzantine process sends arbitrary messages to every other process. An effective fuzzy decision making approach is proposed to eliminate the Byzantine behaviour of the services in the distributed environment. It is proposed to derive a fuzzy decision set in which the alternatives are ranked with grade of membership and based on that an appropriate decision can be arrived on the messages sent by the different services. A balanced decision is to be taken from the messages received across the services. To accomplish this, Hurwicz criterion is used to balance the optimistic and pessimistic views of the decision makers on different services. Grades of membership for the services are assessed using the non-functional Quality of Service parameters and have been estimated using fuzzy entropy measure which logically ranks the participant services. This approach for decision making is tested by varying the number of processes, varying the number of faulty services, varying the message values sent to different services and considering the variation in the views of the decision makers about the services. The experimental result shows that the decision reached is an enhanced one and in case of conflict, the proposed approach provides a concrete result, whereas decision taken using the Lamport’s algorithm is an arbitrary one.

  15. Fuzzy statistical decision-making theory and applications

    CERN Document Server

    Kabak, Özgür

    2016-01-01

    This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimu...

  16. Fuzziness and fuzzy modelling in Bulgaria's energy policy decision-making dilemma

    International Nuclear Information System (INIS)

    Wang Xingquan

    2006-01-01

    The decision complexity resulting from imprecision in decision variables and parameters, a major difficulty for conventional decision analysis methods, can be relevantly analysed and modelled by fuzzy logic. Bulgaria's nuclear policy decision-making process implicates such complexity of imprecise nature: stakeholders, criteria, measurement, etc. Given the suitable applicability of fuzzy logic in this case, this article tries to offer a concrete fuzzy paradigm including delimitation of decision space, quantification of imprecise variables, and, of course, parameterisation. (author)

  17. Boosted decision trees and applications

    International Nuclear Information System (INIS)

    Coadou, Y.

    2013-01-01

    Decision trees are a machine learning technique more and more commonly used in high energy physics, while it has been widely used in the social sciences. After introducing the concepts of decision trees, this article focuses on its application in particle physics. (authors)

  18. Human decision error (HUMDEE) trees

    International Nuclear Information System (INIS)

    Ostrom, L.T.

    1993-01-01

    Graphical presentations of human actions in incident and accident sequences have been used for many years. However, for the most part, human decision making has been underrepresented in these trees. This paper presents a method of incorporating the human decision process into graphical presentations of incident/accident sequences. This presentation is in the form of logic trees. These trees are called Human Decision Error Trees or HUMDEE for short. The primary benefit of HUMDEE trees is that they graphically illustrate what else the individuals involved in the event could have done to prevent either the initiation or continuation of the event. HUMDEE trees also present the alternate paths available at the operator decision points in the incident/accident sequence. This is different from the Technique for Human Error Rate Prediction (THERP) event trees. There are many uses of these trees. They can be used for incident/accident investigations to show what other courses of actions were available and for training operators. The trees also have a consequence component so that not only the decision can be explored, also the consequence of that decision

  19. Decision and game theory in management with intuitionistic fuzzy sets

    CERN Document Server

    Li, Deng-Feng

    2014-01-01

    The focus of this book is on establishing theories and methods of both decision and game analysis in management using intuitionistic fuzzy sets. It proposes a series of innovative theories, models and methods such as the representation theorem and extension principle of intuitionistic fuzzy sets, ranking methods of intuitionistic fuzzy numbers, non-linear and linear programming methods for intuitionistic fuzzy multi-attribute decision making and (interval-valued) intuitionistic fuzzy matrix games. These theories and methods form the theory system of intuitionistic fuzzy decision making and games, which is not only remarkably different from those of the traditional, Bayes and/or fuzzy decision theory but can also provide an effective and efficient tool for solving complex management problems. Since there is a certain degree of inherent hesitancy in real-life management, which cannot always be described by the traditional mathematical methods and/or fuzzy set theory, this book offers an effective approach to us...

  20. Hesitant fuzzy methods for multiple criteria decision analysis

    CERN Document Server

    Zhang, Xiaolu

    2017-01-01

    The book offers a comprehensive introduction to methods for solving multiple criteria decision making and group decision making problems with hesitant fuzzy information. It reports on the authors’ latest research, as well as on others’ research, providing readers with a complete set of decision making tools, such as hesitant fuzzy TOPSIS, hesitant fuzzy TODIM, hesitant fuzzy LINMAP, hesitant fuzzy QUALIFEX, and the deviation modeling approach with heterogeneous fuzzy information. The main focus is on decision making problems in which the criteria values and/or the weights of criteria are not expressed in crisp numbers but are more suitable to be denoted as hesitant fuzzy elements. The largest part of the book is devoted to new methods recently developed by the authors to solve decision making problems in situations where the available information is vague or hesitant. These methods are presented in detail, together with their application to different type of decision-making problems. All in all, the book ...

  1. Decision-Tree Program

    Science.gov (United States)

    Buntine, Wray

    1994-01-01

    IND computer program introduces Bayesian and Markov/maximum-likelihood (MML) methods and more-sophisticated methods of searching in growing trees. Produces more-accurate class-probability estimates important in applications like diagnosis. Provides range of features and styles with convenience for casual user, fine-tuning for advanced user or for those interested in research. Consists of four basic kinds of routines: data-manipulation, tree-generation, tree-testing, and tree-display. Written in C language.

  2. Induction of Ordinal Decision Trees

    NARCIS (Netherlands)

    J.C. Bioch (Cor); V. Popova (Viara)

    2003-01-01

    textabstractThis paper focuses on the problem of monotone decision trees from the point of view of the multicriteria decision aid methodology (MCDA). By taking into account the preferences of the decision maker, an attempt is made to bring closer similar research within machine learning and MCDA.

  3. Decision trees in epidemiological research

    Directory of Open Access Journals (Sweden)

    Ashwini Venkatasubramaniam

    2017-09-01

    Full Text Available Abstract Background In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods. Main text We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART technique and the newer Conditional Inference tree (CTree technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees. Conclusions Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation.

  4. Decision trees in epidemiological research.

    Science.gov (United States)

    Venkatasubramaniam, Ashwini; Wolfson, Julian; Mitchell, Nathan; Barnes, Timothy; JaKa, Meghan; French, Simone

    2017-01-01

    In many studies, it is of interest to identify population subgroups that are relatively homogeneous with respect to an outcome. The nature of these subgroups can provide insight into effect mechanisms and suggest targets for tailored interventions. However, identifying relevant subgroups can be challenging with standard statistical methods. We review the literature on decision trees, a family of techniques for partitioning the population, on the basis of covariates, into distinct subgroups who share similar values of an outcome variable. We compare two decision tree methods, the popular Classification and Regression tree (CART) technique and the newer Conditional Inference tree (CTree) technique, assessing their performance in a simulation study and using data from the Box Lunch Study, a randomized controlled trial of a portion size intervention. Both CART and CTree identify homogeneous population subgroups and offer improved prediction accuracy relative to regression-based approaches when subgroups are truly present in the data. An important distinction between CART and CTree is that the latter uses a formal statistical hypothesis testing framework in building decision trees, which simplifies the process of identifying and interpreting the final tree model. We also introduce a novel way to visualize the subgroups defined by decision trees. Our novel graphical visualization provides a more scientifically meaningful characterization of the subgroups identified by decision trees. Decision trees are a useful tool for identifying homogeneous subgroups defined by combinations of individual characteristics. While all decision tree techniques generate subgroups, we advocate the use of the newer CTree technique due to its simplicity and ease of interpretation.

  5. Objective consensus from decision trees

    International Nuclear Information System (INIS)

    Putora, Paul Martin; Panje, Cedric M; Papachristofilou, Alexandros; Pra, Alan Dal; Hundsberger, Thomas; Plasswilm, Ludwig

    2014-01-01

    Consensus-based approaches provide an alternative to evidence-based decision making, especially in situations where high-level evidence is limited. Our aim was to demonstrate a novel source of information, objective consensus based on recommendations in decision tree format from multiple sources. Based on nine sample recommendations in decision tree format a representative analysis was performed. The most common (mode) recommendations for each eventuality (each permutation of parameters) were determined. The same procedure was applied to real clinical recommendations for primary radiotherapy for prostate cancer. Data was collected from 16 radiation oncology centres, converted into decision tree format and analyzed in order to determine the objective consensus. Based on information from multiple sources in decision tree format, treatment recommendations can be assessed for every parameter combination. An objective consensus can be determined by means of mode recommendations without compromise or confrontation among the parties. In the clinical example involving prostate cancer therapy, three parameters were used with two cut-off values each (Gleason score, PSA, T-stage) resulting in a total of 27 possible combinations per decision tree. Despite significant variations among the recommendations, a mode recommendation could be found for specific combinations of parameters. Recommendations represented as decision trees can serve as a basis for objective consensus among multiple parties

  6. Objective consensus from decision trees.

    Science.gov (United States)

    Putora, Paul Martin; Panje, Cedric M; Papachristofilou, Alexandros; Dal Pra, Alan; Hundsberger, Thomas; Plasswilm, Ludwig

    2014-12-05

    Consensus-based approaches provide an alternative to evidence-based decision making, especially in situations where high-level evidence is limited. Our aim was to demonstrate a novel source of information, objective consensus based on recommendations in decision tree format from multiple sources. Based on nine sample recommendations in decision tree format a representative analysis was performed. The most common (mode) recommendations for each eventuality (each permutation of parameters) were determined. The same procedure was applied to real clinical recommendations for primary radiotherapy for prostate cancer. Data was collected from 16 radiation oncology centres, converted into decision tree format and analyzed in order to determine the objective consensus. Based on information from multiple sources in decision tree format, treatment recommendations can be assessed for every parameter combination. An objective consensus can be determined by means of mode recommendations without compromise or confrontation among the parties. In the clinical example involving prostate cancer therapy, three parameters were used with two cut-off values each (Gleason score, PSA, T-stage) resulting in a total of 27 possible combinations per decision tree. Despite significant variations among the recommendations, a mode recommendation could be found for specific combinations of parameters. Recommendations represented as decision trees can serve as a basis for objective consensus among multiple parties.

  7. Method of reliability allocation based on fault tree analysis and fuzzy math in nuclear power plants

    International Nuclear Information System (INIS)

    Chen Zhaobing; Deng Jian; Cao Xuewu

    2005-01-01

    Reliability allocation is a kind of a difficult multi-objective optimization problem. It can not only be applied to determine the reliability characteristic of reactor systems, subsystem and main components but also be performed to improve the design, operation and maintenance of nuclear plants. The fuzzy math known as one of the powerful tools for fuzzy optimization and the fault analysis deemed to be one of the effective methods of reliability analysis can be applied to the reliability allocation model so as to work out the problems of fuzzy characteristic of some factors and subsystem's choice respectively in this paper. Thus we develop a failure rate allocation model on the basis of the fault tree analysis and fuzzy math. For the choice of the reliability constraint factors, we choose the six important ones according to practical need for conducting the reliability allocation. The subsystem selected by the top-level fault tree analysis is to avoid allocating reliability for all the equipment and components including the unnecessary parts. During the reliability process, some factors can be calculated or measured quantitatively while others only can be assessed qualitatively by the expert rating method. So we adopt fuzzy decision and dualistic contrast to realize the reliability allocation with the help of fault tree analysis. Finally the example of the emergency diesel generator's reliability allocation is used to illustrate reliability allocation model and improve this model simple and applicable. (authors)

  8. An extension of fuzzy decision maps for multi-criteria decision-making

    OpenAIRE

    Elomda, Basem Mohamed; Hefny, Hesham Ahmed; Hassan, Hesham Ahmed

    2013-01-01

    This paper presents a new extension to Fuzzy Decision Maps (FDMs) by allowing use of fuzzy linguistic values to represent relative importance among criteria in the preference matrix as well as representing relative influence among criteria for computing the steady-state matrix in the stage of Fuzzy Cognitive Map (FCM). The proposed model is called the Linguistic Fuzzy Decision Networks (LFDNs). The proposed LFDN provides considerable flexibility to decision makers when solving real world Mult...

  9. Algorithms for Decision Tree Construction

    KAUST Repository

    Chikalov, Igor

    2011-01-01

    The study of algorithms for decision tree construction was initiated in 1960s. The first algorithms are based on the separation heuristic [13, 31] that at each step tries dividing the set of objects as evenly as possible. Later Garey and Graham [28] showed that such algorithm may construct decision trees whose average depth is arbitrarily far from the minimum. Hyafil and Rivest in [35] proved NP-hardness of DT problem that is constructing a tree with the minimum average depth for a diagnostic problem over 2-valued information system and uniform probability distribution. Cox et al. in [22] showed that for a two-class problem over information system, even finding the root node attribute for an optimal tree is an NP-hard problem. © Springer-Verlag Berlin Heidelberg 2011.

  10. Fuzzy Bi-level Decision-Making Techniques: A Survey

    Directory of Open Access Journals (Sweden)

    Guangquan Zhang

    2016-04-01

    Full Text Available Bi-level decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a bi-level hierarchy. A challenge in handling bi-level decision problems is that various uncertainties naturally appear in decision-making process. Significant efforts have been devoted that fuzzy set techniques can be used to effectively deal with uncertain issues in bi-level decision-making, known as fuzzy bi-level decision-making techniques, and researchers have successfully gained experience in this area. It is thus vital that an instructive review of current trends in this area should be conducted, not only of the theoretical research but also the practical developments. This paper systematically reviews up-to-date fuzzy bi-level decisionmaking techniques, including models, approaches, algorithms and systems. It also clusters related technique developments into four main categories: basic fuzzy bi-level decision-making, fuzzy bi-level decision-making with multiple optima, fuzzy random bi-level decision-making, and the applications of bi-level decision-making techniques in different domains. By providing state-of-the-art knowledge, this survey paper will directly support researchers and practitioners in their understanding of developments in theoretical research results and applications in relation to fuzzy bi-level decision-making techniques.

  11. Finding small equivalent decision trees is hard

    NARCIS (Netherlands)

    Zantema, H.; Bodlaender, H.L.

    2000-01-01

    Two decision trees are called decision equivalent if they represent the same function, i.e., they yield the same result for every possible input. We prove that given a decision tree and a number, to decide if there is a decision equivalent decision tree of size at most that number is NPcomplete. As

  12. A generic methodology for developing fuzzy decision models

    NARCIS (Netherlands)

    Bosma, R.H.; Berg, van den J.; Kaymak, Uzay; Udo, H.M.J.; Verreth, J.A.J.

    2012-01-01

    An important paradigm in decision-making models is utility-maximization where most models do not include actors’ motives. Fuzzy set theory on the other hand offers a method to simulate human decision-making. However, the literature describing expert-driven fuzzy logic models, rarely gives precise

  13. Representing Boolean Functions by Decision Trees

    KAUST Repository

    Chikalov, Igor

    2011-01-01

    A Boolean or discrete function can be represented by a decision tree. A compact form of decision tree named binary decision diagram or branching program is widely known in logic design [2, 40]. This representation is equivalent to other forms

  14. Interpreting CNNs via Decision Trees

    OpenAIRE

    Zhang, Quanshi; Yang, Yu; Wu, Ying Nian; Zhu, Song-Chun

    2018-01-01

    This paper presents a method to learn a decision tree to quantitatively explain the logic of each prediction of a pre-trained convolutional neural networks (CNNs). Our method boosts the following two aspects of network interpretability. 1) In the CNN, each filter in a high conv-layer must represent a specific object part, instead of describing mixed patterns without clear meanings. 2) People can explain each specific prediction made by the CNN at the semantic level using a decision tree, i.e....

  15. Fuzzy probability based fault tree analysis to propagate and quantify epistemic uncertainty

    International Nuclear Information System (INIS)

    Purba, Julwan Hendry; Sony Tjahyani, D.T.; Ekariansyah, Andi Sofrany; Tjahjono, Hendro

    2015-01-01

    Highlights: • Fuzzy probability based fault tree analysis is to evaluate epistemic uncertainty in fuzzy fault tree analysis. • Fuzzy probabilities represent likelihood occurrences of all events in a fault tree. • A fuzzy multiplication rule quantifies epistemic uncertainty of minimal cut sets. • A fuzzy complement rule estimate epistemic uncertainty of the top event. • The proposed FPFTA has successfully evaluated the U.S. Combustion Engineering RPS. - Abstract: A number of fuzzy fault tree analysis approaches, which integrate fuzzy concepts into the quantitative phase of conventional fault tree analysis, have been proposed to study reliabilities of engineering systems. Those new approaches apply expert judgments to overcome the limitation of the conventional fault tree analysis when basic events do not have probability distributions. Since expert judgments might come with epistemic uncertainty, it is important to quantify the overall uncertainties of the fuzzy fault tree analysis. Monte Carlo simulation is commonly used to quantify the overall uncertainties of conventional fault tree analysis. However, since Monte Carlo simulation is based on probability distribution, this technique is not appropriate for fuzzy fault tree analysis, which is based on fuzzy probabilities. The objective of this study is to develop a fuzzy probability based fault tree analysis to overcome the limitation of fuzzy fault tree analysis. To demonstrate the applicability of the proposed approach, a case study is performed and its results are then compared to the results analyzed by a conventional fault tree analysis. The results confirm that the proposed fuzzy probability based fault tree analysis is feasible to propagate and quantify epistemic uncertainties in fault tree analysis

  16. Algorithms for Decision Tree Construction

    KAUST Repository

    Chikalov, Igor

    2011-01-01

    The study of algorithms for decision tree construction was initiated in 1960s. The first algorithms are based on the separation heuristic [13, 31] that at each step tries dividing the set of objects as evenly as possible. Later Garey and Graham [28

  17. Decision tree modeling using R.

    Science.gov (United States)

    Zhang, Zhongheng

    2016-08-01

    In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.

  18. Using fuzzy mathematics for decision making in economics

    Directory of Open Access Journals (Sweden)

    Pavkov Ivan

    2012-01-01

    Full Text Available Traditionally, economic models are based on classical mathematics and Aristotelian two-valued logic. Nevertheless, fuzzy mathematics, as a tool for modeling some types of uncertainties and incomplete phenomena, is a more appropriate framework for modeling in economics. New approach has resulted in approximate reasoning and fuzzy control systems, which proved to be an efficient tool for decision making in fuzzy environment.

  19. MULTIPLE CRITERIA DECISION MAKING APPROACH FOR INDUSTRIAL ENGINEER SELECTION USING FUZZY AHP-FUZZY TOPSIS

    OpenAIRE

    Deliktaş, Derya; ÜSTÜN, Özden

    2018-01-01

    In this study, a fuzzy multiple criteria decision-making approach is proposed to select an industrial engineer among ten candidates in a manufacturing environment. The industrial engineer selection problem is a special case of the personal selection problem. This problem, which has hierarchical structure of criteria and many decision makers, contains many criteria. The evaluation process of decision makers also includes ambiguous parameters. The fuzzy AHP is used to determin...

  20. Totally optimal decision trees for Boolean functions

    KAUST Repository

    Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2016-01-01

    We study decision trees which are totally optimal relative to different sets of complexity parameters for Boolean functions. A totally optimal tree is an optimal tree relative to each parameter from the set simultaneously. We consider the parameters

  1. A generic methodology for developing fuzzy decision models

    NARCIS (Netherlands)

    Bosma, R.; Berg, van den J.; Kaymak, U.; Udo, H.; Verreth, J.

    2012-01-01

    An important paradigm in decision-making models is utility-maximization where most models do not include actors’ motives. Fuzzy set theory on the other hand offers a method to simulate human decisionmaking. However, the literature describing expert-driven fuzzy logic models, rarely gives precise

  2. Geo-Spatial Tactical Decision Aid Systems: Fuzzy Logic for Supporting Decision Making

    National Research Council Canada - National Science Library

    Grasso, Raffaele; Giannecchini, Simone

    2006-01-01

    .... This paper describes a tactical decision aid system based on fuzzy logic reasoning for data fusion and on current Open Geospatial Consortium specifications for interoperability, data dissemination...

  3. Fuzzy-like multiple objective multistage decision making

    CERN Document Server

    Xu, Jiuping

    2014-01-01

    Decision has inspired reflection of many thinkers since the ancient times. With the rapid development of science and society, appropriate dynamic decision making has been playing an increasingly important role in many areas of human activity including engineering, management, economy and others. In most real-world problems, decision makers usually have to make decisions sequentially at different points in time and space, at different levels for a component or a system, while facing multiple and conflicting objectives and a hybrid uncertain environment where fuzziness and randomness co-exist in a decision making process. This leads to the development of fuzzy-like multiple objective multistage decision making. This book provides a thorough understanding of the concepts of dynamic optimization from a modern perspective and presents the state-of-the-art methodology for modeling, analyzing and solving the most typical multiple objective multistage decision making practical application problems under fuzzy-like un...

  4. Totally optimal decision trees for Boolean functions

    KAUST Repository

    Chikalov, Igor

    2016-07-28

    We study decision trees which are totally optimal relative to different sets of complexity parameters for Boolean functions. A totally optimal tree is an optimal tree relative to each parameter from the set simultaneously. We consider the parameters characterizing both time (in the worst- and average-case) and space complexity of decision trees, i.e., depth, total path length (average depth), and number of nodes. We have created tools based on extensions of dynamic programming to study totally optimal trees. These tools are applicable to both exact and approximate decision trees, and allow us to make multi-stage optimization of decision trees relative to different parameters and to count the number of optimal trees. Based on the experimental results we have formulated the following hypotheses (and subsequently proved): for almost all Boolean functions there exist totally optimal decision trees (i) relative to the depth and number of nodes, and (ii) relative to the depth and average depth.

  5. Meta-learning in decision tree induction

    CERN Document Server

    Grąbczewski, Krzysztof

    2014-01-01

    The book focuses on different variants of decision tree induction but also describes  the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimen...

  6. Decentralized Channel Decisions of Green Supply Chain in a Fuzzy Decision Making Environment

    Directory of Open Access Journals (Sweden)

    Shengju Sang

    2017-01-01

    Full Text Available This paper considers the greening policies in a decentralized channel between one manufacturer and one retailer in a fuzzy decision making environment. We consider the manufacturing cost and the parameters of demand function as the fuzzy variables. Based on the different market structures, we develop three different fuzzy decentralized decision models. For each case, the expected value, optimistic value and pessimistic value models are formulated, and their optimal solutions are also derived through the fuzzy set theory. Finally, three numerical examples are solved to examine the effectiveness of fuzzy models. The effects of the confidence level of the supply chain memberrs profits and the fuzziness of parameters on optimal prices, level of green innovation, and fuzzy expected profits of actors are also analyzed.

  7. Fuzzy multiple objective decision making methods and applications

    CERN Document Server

    Lai, Young-Jou

    1994-01-01

    In the last 25 years, the fuzzy set theory has been applied in many disciplines such as operations research, management science, control theory, artificial intelligence/expert system, etc. In this volume, methods and applications of crisp, fuzzy and possibilistic multiple objective decision making are first systematically and thoroughly reviewed and classified. This state-of-the-art survey provides readers with a capsule look into the existing methods, and their characteristics and applicability to analysis of fuzzy and possibilistic programming problems. To realize practical fuzzy modelling, it presents solutions for real-world problems including production/manufacturing, location, logistics, environment management, banking/finance, personnel, marketing, accounting, agriculture economics and data analysis. This book is a guided tour through the literature in the rapidly growing fields of operations research and decision making and includes the most up-to-date bibliographical listing of literature on the topi...

  8. Fuzzy Specification in Real Estate Market Decision Making

    Directory of Open Access Journals (Sweden)

    Victoria Lopez

    2010-04-01

    Full Text Available In this paper we present a software tool designed as a decision aid system for all actors being involved when buying or selling real state, client and realtor, where a main objective for the commercial is to concentrate the client preferences into few alternatives. Since the required previous analysis implies a number of fuzzy concepts, the general procedure here presented considers fuzzy logic to deal with specifications. As a consequence, time devoted to elicitation and requirement analysis is reduced.

  9. Creating ensembles of decision trees through sampling

    Science.gov (United States)

    Kamath, Chandrika; Cantu-Paz, Erick

    2005-08-30

    A system for decision tree ensembles that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in ensembles.

  10. Representing Boolean Functions by Decision Trees

    KAUST Repository

    Chikalov, Igor

    2011-01-01

    A Boolean or discrete function can be represented by a decision tree. A compact form of decision tree named binary decision diagram or branching program is widely known in logic design [2, 40]. This representation is equivalent to other forms, and in some cases it is more compact than values table or even the formula [44]. Representing a function in the form of decision tree allows applying graph algorithms for various transformations [10]. Decision trees and branching programs are used for effective hardware [15] and software [5] implementation of functions. For the implementation to be effective, the function representation should have minimal time and space complexity. The average depth of decision tree characterizes the expected computing time, and the number of nodes in branching program characterizes the number of functional elements required for implementation. Often these two criteria are incompatible, i.e. there is no solution that is optimal on both time and space complexity. © Springer-Verlag Berlin Heidelberg 2011.

  11. Analysis of event tree with imprecise inputs by fuzzy set theory

    International Nuclear Information System (INIS)

    Ahn, Kwang Il; Chun, Moon Hyun

    1990-01-01

    Fuzzy set theory approach is proposed as a method to analyze event trees with imprecise or linguistic input variables such as 'likely' or 'improbable' instead of the numerical probability. In this paper, it is shown how the fuzzy set theory can be applied to the event tree analysis. The result of this study shows that the fuzzy set theory approach can be applied as an acceptable and effective tool for analysis of the event tree with fuzzy type of inputs. Comparisons of the fuzzy theory approach with the probabilistic approach of computing probabilities of final states of the event tree through subjective weighting factors and LHS technique show that the two approaches have common factors and give reasonable results

  12. Application of fuzzy decision-making method in nuclear emergency

    International Nuclear Information System (INIS)

    Xu Zhixin; Xi Shuren; Qu Jingyuan

    2005-01-01

    Protective actions such as evacuation, sheltering and iodine administration can be taken to mitigate the radiological consequence in the event of an accidental release. In general, decision-making of countermeasures involves both quantitative and qualitative criteria. The conventional approaches to assessing these criteria tend to be less effective when dealing with those qualitative criteria that are imprecise or vague. In this regard, fuzzy set method is an alternative tool. It can cope with vague assessment in a better way. This paper presents the application of fussy methodology to decision-making of protective actions in nuclear emergencies. In this method linguistic terms and fuzzy triangular numbers are used to represent decision-maker's subjective assessment for different decision criteria considered and decision alternatives versus the decision criteria. Following the assessment performed by specialists, corresponding evaluations can be synthesized and ranked. Finally, the optimal strategy for implementing protective actions can be recommended. (authors)

  13. VC-dimension of univariate decision trees.

    Science.gov (United States)

    Yildiz, Olcay Taner

    2015-02-01

    In this paper, we give and prove the lower bounds of the Vapnik-Chervonenkis (VC)-dimension of the univariate decision tree hypothesis class. The VC-dimension of the univariate decision tree depends on the VC-dimension values of its subtrees and the number of inputs. Via a search algorithm that calculates the VC-dimension of univariate decision trees exhaustively, we show that our VC-dimension bounds are tight for simple trees. To verify that the VC-dimension bounds are useful, we also use them to get VC-generalization bounds for complexity control using structural risk minimization in decision trees, i.e., pruning. Our simulation results show that structural risk minimization pruning using the VC-dimension bounds finds trees that are more accurate as those pruned using cross validation.

  14. Permutation based decision making under fuzzy environment using Tabu search

    Directory of Open Access Journals (Sweden)

    Mahdi Bashiri

    2012-04-01

    Full Text Available One of the techniques, which are used for Multiple Criteria Decision Making (MCDM is the permutation. In the classical form of permutation, it is assumed that weights and decision matrix components are crisp. However, when group decision making is under consideration and decision makers could not agree on a crisp value for weights and decision matrix components, fuzzy numbers should be used. In this article, the fuzzy permutation technique for MCDM problems has been explained. The main deficiency of permutation is its big computational time, so a Tabu Search (TS based algorithm has been proposed to reduce the computational time. A numerical example has illustrated the proposed approach clearly. Then, some benchmark instances extracted from literature are solved by proposed TS. The analyses of the results show the proper performance of the proposed method.

  15. Fuzzy group decision making in a competetive situation

    NARCIS (Netherlands)

    Yan, Jiang; Yan, J.; van Harten, Aart; van der Wegen, Leonardus L.M.

    1997-01-01

    In this paper a group decision making problem in a competitive situation with two opponents is considered. Uncertainty in the score assessment for both opponents of any individual of the group as well as between group members is taken into account by means of fuzzy sets. The individual scores can be

  16. Decision Tree Technique for Particle Identification

    International Nuclear Information System (INIS)

    Quiller, Ryan

    2003-01-01

    Particle identification based on measurements such as the Cerenkov angle, momentum, and the rate of energy loss per unit distance (-dE/dx) is fundamental to the BaBar detector for particle physics experiments. It is particularly important to separate the charged forms of kaons and pions. Currently, the Neural Net, an algorithm based on mapping input variables to an output variable using hidden variables as intermediaries, is one of the primary tools used for identification. In this study, a decision tree classification technique implemented in the computer program, CART, was investigated and compared to the Neural Net over the range of momenta, 0.25 GeV/c to 5.0 GeV/c. For a given subinterval of momentum, three decision trees were made using different sets of input variables. The sensitivity and specificity were calculated for varying kaon acceptance thresholds. This data was used to plot Receiver Operating Characteristic curves (ROC curves) to compare the performance of the classification methods. Also, input variables used in constructing the decision trees were analyzed. It was found that the Neural Net was a significant contributor to decision trees using dE/dx and the Cerenkov angle as inputs. Furthermore, the Neural Net had poorer performance than the decision tree technique, but tended to improve decision tree performance when used as an input variable. These results suggest that the decision tree technique using Neural Net input may possibly increase accuracy of particle identification in BaBar

  17. Fuzzy multiple attribute decision making methods and applications

    CERN Document Server

    Chen, Shu-Jen

    1992-01-01

    This monograph is intended for an advanced undergraduate or graduate course as well as for researchers, who want a compilation of developments in this rapidly growing field of operations research. This is a sequel to our previous works: "Multiple Objective Decision Making--Methods and Applications: A state-of-the-Art Survey" (No.164 of the Lecture Notes); "Multiple Attribute Decision Making--Methods and Applications: A State-of-the-Art Survey" (No.186 of the Lecture Notes); and "Group Decision Making under Multiple Criteria--Methods and Applications" (No.281 of the Lecture Notes). In this monograph, the literature on methods of fuzzy Multiple Attribute Decision Making (MADM) has been reviewed thoroughly and critically, and classified systematically. This study provides readers with a capsule look into the existing methods, their characteristics, and applicability to the analysis of fuzzy MADM problems. The basic concepts and algorithms from the classical MADM methods have been used in the development of the f...

  18. Fuzzy rationality and parameter elicitation in decision analysis

    Science.gov (United States)

    Nikolova, Natalia D.; Tenekedjiev, Kiril I.

    2010-07-01

    It is widely recognised by decision analysts that real decision-makers always make estimates in an interval form. An overview of techniques to find an optimal alternative among such with imprecise and interval probabilities is presented. Scalarisation methods are outlined as most appropriate. A proper continuation of such techniques is fuzzy rational (FR) decision analysis. A detailed representation of the elicitation process influenced by fuzzy rationality is given. The interval character of probabilities leads to the introduction of ribbon functions, whose general form and special cases are compared with the p-boxes. As demonstrated, approximation of utilities in FR decision analysis does not depend on the probabilities, but the approximation of probabilities is dependent on preferences.

  19. Fuzzy Privacy Decision for Context-Aware Access Personal Information

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qingsheng; QI Yong; ZHAO Jizhong; HOU Di; NIU Yujie

    2007-01-01

    A context-aware privacy protection framework was designed for context-aware services and privacy control methods about access personal information in pervasive environment. In the process of user's privacy decision, it can produce fuzzy privacy decision as the change of personal information sensitivity and personal information receiver trust. The uncertain privacy decision model was proposed about personal information disclosure based on the change of personal information receiver trust and personal information sensitivity. A fuzzy privacy decision information system was designed according to this model. Personal privacy control policies can be extracted from this information system by using rough set theory. It also solves the problem about learning privacy control policies of personal information disclosure.

  20. The decision tree approach to classification

    Science.gov (United States)

    Wu, C.; Landgrebe, D. A.; Swain, P. H.

    1975-01-01

    A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

  1. Comprehensive decision tree models in bioinformatics.

    Directory of Open Access Journals (Sweden)

    Gregor Stiglic

    Full Text Available PURPOSE: Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. METHODS: This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. RESULTS: The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. CONCLUSIONS: The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets

  2. Comprehensive decision tree models in bioinformatics.

    Science.gov (United States)

    Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

    2012-01-01

    Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly

  3. RE-Powering’s Electronic Decision Tree

    Science.gov (United States)

    Developed by US EPA's RE-Powering America's Land Initiative, the RE-Powering Decision Trees tool guides interested parties through a process to screen sites for their suitability for solar photovoltaics or wind installations

  4. Speech Recognition Using Randomized Relational Decision Trees

    National Research Council Canada - National Science Library

    Amit, Yali

    1999-01-01

    .... This implies that we recognize words as units, without recognizing their subcomponents. Multiple randomized decision trees are used to access the large pool of acoustic events in a systematic manner and are aggregated to produce the classifier.

  5. PRIA 3 Fee Determination Decision Tree

    Science.gov (United States)

    The PRIA 3 decision tree will help applicants requesting a pesticide registration or certain tolerance action to accurately identify the category of their application and the amount of the required fee before they submit the application.

  6. Solar and Wind Site Screening Decision Trees

    Science.gov (United States)

    EPA and NREL created a decision tree to guide state and local governments and other stakeholders through a process for screening sites for their suitability for future redevelopment with solar photovoltaic (PV) energy and wind energy.

  7. FUZZY DECISION ANALYSIS FOR INTEGRATED ENVIRONMENTAL VULNERABILITY ASSESSMENT OF THE MID-ATLANTIC REGION

    Science.gov (United States)

    A fuzzy decision analysis method for integrating ecological indicators is developed. This is a combination of a fuzzy ranking method and the Analytic Hierarchy Process (AHP). The method is capable ranking ecosystems in terms of environmental conditions and suggesting cumula...

  8. A survey of decision tree classifier methodology

    Science.gov (United States)

    Safavian, S. R.; Landgrebe, David

    1991-01-01

    Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

  9. Parallel object-oriented decision tree system

    Science.gov (United States)

    Kamath,; Chandrika, Cantu-Paz [Dublin, CA; Erick, [Oakland, CA

    2006-02-28

    A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.

  10. A fuzzy decision making method for outsourcing activities

    Directory of Open Access Journals (Sweden)

    Zahra Afrandkhalilabad

    2012-09-01

    Full Text Available Optimization of outsourcing operations plays an important role on development and progress for modern organizations. One important question in optimization process is to find a tradeoff between advantage and disadvantage of outsourcing and make appropriate decision whenever outsourcing action is necessary. In fact, there are several cases where outsourcing is not implemented properly and organizations suffer from the consequences. The primary purpose of this paper is to investigate various aspects of outsourcing to facilitate decision-making process in fuzzy environments. The preliminary results detect some of the necessary actions for decision making operations.DOI: 10.5267/j.msl.2012.10.005Keywords:

  11. Fuzzy Multicriteria Decision Analysis for Adaptive Watershed Management

    Science.gov (United States)

    Chang, N.

    2006-12-01

    The dramatic changes of societal complexity due to intensive interactions among agricultural, industrial, and municipal sectors have resulted in acute issues of water resources redistribution and water quality management in many river basins. Given the fact that integrated watershed management is more a political and societal than a technical challenge, there is a need for developing a compelling method leading to justify a water-based land use program in some critical regions. Adaptive watershed management is viewed as an indispensable tool nowadays for providing step-wise constructive decision support that is concerned with all related aspects of the water consumption cycle and those facilities affecting water quality and quantity temporally and spatially. Yet the greatest challenge that decision makers face today is to consider how to leverage ambiguity, paradox, and uncertainty to their competitive advantage of management policy quantitatively. This paper explores a fuzzy multicriteria evaluation method for water resources redistribution and subsequent water quality management with respect to a multipurpose channel-reservoir system--the Tseng- Wen River Basin, South Taiwan. Four fuzzy operators tailored for this fuzzy multicriteria decision analysis depict greater flexibility in representing the complexity of various possible trade-offs among management alternatives constrained by physical, economic, and technical factors essential for adaptive watershed management. The management strategies derived may enable decision makers to integrate a vast number of internal weirs, water intakes, reservoirs, drainage ditches, transfer pipelines, and wastewater treatment facilities within the basin and bring up the permitting issue for transboundary diversion from a neighboring river basin. Experience gained indicates that the use of different types of fuzzy operators is highly instructive, which also provide unique guidance collectively for achieving the overarching goals

  12. Six Sigma Project Selection Using Fuzzy TOPSIS Decision Making Approach

    Directory of Open Access Journals (Sweden)

    Rajeev Rathi

    2015-05-01

    Full Text Available Six Sigma is considered as a logical business strategy that attempts to identify and eliminate the defects or failures for improving the quality of product and processes. A decision on project selection in Six Sigma is always very critical; it plays a key role in successful implementation of Six Sigma. Selection of a right Six Sigma project is essentially important for an automotive company because it greatly influences the manufacturing costs. This paper discusses an approach for right Six Sigma project selection at an automotive industry using fuzzy logic based TOPSIS method. The fuzzy TOPSIS is a well recognized tool to undertake the fuzziness of the data involved in choosing the right preferences. In this context, evaluation criteria have been designed for selection of best alternative. The weights of evaluation criteria are calculated by using the MDL (modified digital logic method and final ranking is calculated through priority index obtained by using fuzzy TOPSIS method. In the selected case study, this approach has rightly helped to identify the right project for implementing Six Sigma for achieving improvement in productivity.

  13. Fast Image Texture Classification Using Decision Trees

    Science.gov (United States)

    Thompson, David R.

    2011-01-01

    Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

  14. Fuzzy Decision Support in the Early Phases of the Fuzzy Front End of Innovation in Product Development

    DEFF Research Database (Denmark)

    Achiche, Sofiane; Appio, Francesco Paolo

    2010-01-01

    Opportunity Identification and Opportunity Analysis. This is achieved by analyzing the Influencing Factors (Firm context, Industry context, Macro environment) along with data collection from managers followed by the automatic construction of fuzzy decision support models (FDSM) of the discovered relationships...

  15. Algorithms for optimal dyadic decision trees

    Energy Technology Data Exchange (ETDEWEB)

    Hush, Don [Los Alamos National Laboratory; Porter, Reid [Los Alamos National Laboratory

    2009-01-01

    A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.

  16. Automated Decision Tree Classification of Corneal Shape

    Science.gov (United States)

    Twa, Michael D.; Parthasarathy, Srinivasan; Roberts, Cynthia; Mahmoud, Ashraf M.; Raasch, Thomas W.; Bullimore, Mark A.

    2011-01-01

    Purpose The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusions Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification

  17. Decision and Inhibitory Trees for Decision Tables with Many-Valued Decisions

    KAUST Repository

    Azad, Mohammad

    2018-06-06

    Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set of decision trees for the given decision table which was previously only attainable by brute-force algorithms. We study decision tables with many-valued decisions (each row may contain multiple decisions) because they are more reasonable models of data in many cases. To address this problem in a broad sense, we consider not only decision trees but also inhibitory trees where terminal nodes are labeled with “̸= decision”. Inhibitory trees can sometimes describe more knowledge from datasets than decision trees. As for cost functions, we consider depth or average depth to minimize time complexity of trees, and the number of nodes or the number of the terminal, or nonterminal nodes to minimize the space complexity of trees. We investigate the multi-stage optimization of trees relative to some cost functions, and also the possibility to describe the whole set of strictly optimal trees. Furthermore, we study the bi-criteria optimization cost vs. cost and cost vs. uncertainty for decision trees, and cost vs. cost and cost vs. completeness for inhibitory trees. The most interesting application of the developed technique is the creation of multi-pruning and restricted multi-pruning approaches which are useful for knowledge representation and prediction. The experimental results show that decision trees constructed by these approaches can often outperform the decision trees constructed by the CART algorithm. Another application includes the comparison of 12 greedy heuristics for single- and bi-criteria optimization (cost vs. cost) of trees. We also study the three approaches (decision tables with many-valued decisions, decision tables with most common decisions, and decision tables with generalized decisions) to handle

  18. Decision Trees for Helpdesk Advisor Graphs

    OpenAIRE

    Gkezerlis, Spyros; Kalles, Dimitris

    2017-01-01

    We use decision trees to build a helpdesk agent reference network to facilitate the on-the-job advising of junior or less experienced staff on how to better address telecommunication customer fault reports. Such reports generate field measurements and remote measurements which, when coupled with location data and client attributes, and fused with organization-level statistics, can produce models of how support should be provided. Beyond decision support, these models can help identify staff w...

  19. Minimization of Decision Tree Average Depth for Decision Tables with Many-valued Decisions

    KAUST Repository

    Azad, Mohammad

    2014-09-13

    The paper is devoted to the analysis of greedy algorithms for the minimization of average depth of decision trees for decision tables such that each row is labeled with a set of decisions. The goal is to find one decision from the set of decisions. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of average depth of decision trees.

  20. Minimization of Decision Tree Average Depth for Decision Tables with Many-valued Decisions

    KAUST Repository

    Azad, Mohammad; Moshkov, Mikhail

    2014-01-01

    The paper is devoted to the analysis of greedy algorithms for the minimization of average depth of decision trees for decision tables such that each row is labeled with a set of decisions. The goal is to find one decision from the set of decisions. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of average depth of decision trees.

  1. Two Trees: Migrating Fault Trees to Decision Trees for Real Time Fault Detection on International Space Station

    Science.gov (United States)

    Lee, Charles; Alena, Richard L.; Robinson, Peter

    2004-01-01

    We started from ISS fault trees example to migrate to decision trees, presented a method to convert fault trees to decision trees. The method shows that the visualizations of root cause of fault are easier and the tree manipulating becomes more programmatic via available decision tree programs. The visualization of decision trees for the diagnostic shows a format of straight forward and easy understands. For ISS real time fault diagnostic, the status of the systems could be shown by mining the signals through the trees and see where it stops at. The other advantage to use decision trees is that the trees can learn the fault patterns and predict the future fault from the historic data. The learning is not only on the static data sets but also can be online, through accumulating the real time data sets, the decision trees can gain and store faults patterns in the trees and recognize them when they come.

  2. IND - THE IND DECISION TREE PACKAGE

    Science.gov (United States)

    Buntine, W.

    1994-01-01

    A common approach to supervised classification and prediction in artificial intelligence and statistical pattern recognition is the use of decision trees. A tree is "grown" from data using a recursive partitioning algorithm to create a tree which has good prediction of classes on new data. Standard algorithms are CART (by Breiman Friedman, Olshen and Stone) and ID3 and its successor C4 (by Quinlan). As well as reimplementing parts of these algorithms and offering experimental control suites, IND also introduces Bayesian and MML methods and more sophisticated search in growing trees. These produce more accurate class probability estimates that are important in applications like diagnosis. IND is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or it may be omitted. One of the attributes is delegated the "target" and IND grows trees to predict the target. Prediction can then be done on new data or the decision tree printed out for inspection. IND provides a range of features and styles with convenience for the casual user as well as fine-tuning for the advanced user or those interested in research. IND can be operated in a CART-like mode (but without regression trees, surrogate splits or multivariate splits), and in a mode like the early version of C4. Advanced features allow more extensive search, interactive control and display of tree growing, and Bayesian and MML algorithms for tree pruning and smoothing. These often produce more accurate class probability estimates at the leaves. IND also comes with a comprehensive experimental control suite. IND consists of four basic kinds of routines: data manipulation routines, tree generation routines, tree testing routines, and tree display routines. The data manipulation routines are used to partition a single large data set into smaller training and test sets. The

  3. Hesitant fuzzy soft sets with application in multicriteria group decision making problems.

    Science.gov (United States)

    Wang, Jian-qiang; Li, Xin-E; Chen, Xiao-hong

    2015-01-01

    Soft sets have been regarded as a useful mathematical tool to deal with uncertainty. In recent years, many scholars have shown an intense interest in soft sets and extended standard soft sets to intuitionistic fuzzy soft sets, interval-valued fuzzy soft sets, and generalized fuzzy soft sets. In this paper, hesitant fuzzy soft sets are defined by combining fuzzy soft sets with hesitant fuzzy sets. And some operations on hesitant fuzzy soft sets based on Archimedean t-norm and Archimedean t-conorm are defined. Besides, four aggregation operations, such as the HFSWA, HFSWG, GHFSWA, and GHFSWG operators, are given. Based on these operators, a multicriteria group decision making approach with hesitant fuzzy soft sets is also proposed. To demonstrate its accuracy and applicability, this approach is finally employed to calculate a numerical example.

  4. Pricing and Remanufacturing Decisions of a Decentralized Fuzzy Supply Chain

    Directory of Open Access Journals (Sweden)

    Jing Zhao

    2013-01-01

    costs, and the collecting scaling parameters of the two retailers. The purpose of this paper is to explore how the manufacturer and the two retailers make their own decisions about wholesale price, retail prices, and the remanufacturing rates in the expected value model. Using game theory and fuzzy theory, we examine each firm’s strategy and explore the role of the manufacturer and the two retailers over three different game scenarios. We get some insights into the economic behavior of firms, which can serve as the basis for empirical study in the future.

  5. Application of fuzzy inference system to increase efficiency of management decision-making in agricultural enterprises

    OpenAIRE

    Balanovskаya, Tetiana Ivanovna; Boretska, Zoreslava Petrovna

    2014-01-01

    Application of fuzzy inference system to increase efficiency of management decision- making in agricultural enterprises. Theoretical and methodological issues, practical recommendations on improvement of management decision-making in agricultural enterprises to increase their competitiveness have been intensified and developed in the article. A simulation example of a quality management system for agricultural products on the basis of the theory of fuzzy sets and fuzzy logic has been proposed...

  6. Fuzzy-valued linguistic soft set theory and multi-attribute decision-making application

    International Nuclear Information System (INIS)

    Aiwu, Zhao; Hongjun, Guan

    2016-01-01

    In this work, we propose the theory of fuzzy linguistic soft set (FLSS) to represent the uncertainty and multi-angle of view when decision makers evaluate an object during decision-making. FLSS integrates fuzzy set theory, linguistic variable and soft set theory. It allows decision makers to utilize linguistic variables to evaluate an object and utilize fuzzy values to describe the corresponding grade of their support of their decisions. Meanwhile, because of the flexibility of soft set, decision makers can use more than one pair of fuzzy-linguistic evaluations to express their opinions from multiple perspectives directly, if necessary. Therefore, it is more flexible and practical than traditional fuzzy set or 2-dimension uncertainty linguistic variable. We also develop a generalized weighted aggregation operator for FLSSs to solve corresponding decision-making issues. Finally, we give a numerical example to verify the practicality and effectiveness of the proposed method.

  7. A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision-Making Process in Medicine

    Directory of Open Access Journals (Sweden)

    Jose M. Gonzalez-Cava

    2018-01-01

    Full Text Available One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.

  8. Comparison of Greedy Algorithms for Decision Tree Optimization

    KAUST Repository

    Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail

    2013-01-01

    This chapter is devoted to the study of 16 types of greedy algorithms for decision tree construction. The dynamic programming approach is used for construction of optimal decision trees. Optimization is performed relative to minimal values

  9. On algorithm for building of optimal α-decision trees

    KAUST Repository

    Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail

    2010-01-01

    The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic

  10. Interval-Valued Intuitionistic Fuzzy Multicriteria Group Decision Making Based on VIKOR and Choquet Integral

    Directory of Open Access Journals (Sweden)

    Chunqiao Tan

    2013-01-01

    Full Text Available An effective decision making approach based on VIKOR and Choquet integral is developed to solve multicriteria group decision making problem with conflicting criteria and interdependent subjective preference of decision makers in a fuzzy environment where preferences of decision makers with respect to criteria are represented by interval-valued intuitionistic fuzzy sets. First, an interval-valued intuitionistic fuzzy Choquet integral operator is given. Some of its properties are investigated in detail. The extended VIKOR decision procedure based on the proposed operator is developed for solving the multicriteria group decision making problem where the interactive criteria weight is measured by Shapley value. An illustrative example is given for demonstrating the applicability of the proposed decision procedure for solving the multi-criteria group decision making problem in interval-valued intuitionistic fuzzy environment.

  11. Safety validation of decision trees for hepatocellular carcinoma.

    Science.gov (United States)

    Wang, Xian-Qiang; Liu, Zhe; Lv, Wen-Ping; Luo, Ying; Yang, Guang-Yun; Li, Chong-Hui; Meng, Xiang-Fei; Liu, Yang; Xu, Ke-Sen; Dong, Jia-Hong

    2015-08-21

    To evaluate a different decision tree for safe liver resection and verify its efficiency. A total of 2457 patients underwent hepatic resection between January 2004 and December 2010 at the Chinese PLA General Hospital, and 634 hepatocellular carcinoma (HCC) patients were eligible for the final analyses. Post-hepatectomy liver failure (PHLF) was identified by the association of prothrombin time 50 μmol/L (the "50-50" criteria), which were assessed at day 5 postoperatively or later. The Swiss-Clavien decision tree, Tokyo University-Makuuchi decision tree, and Chinese consensus decision tree were adopted to divide patients into two groups based on those decision trees in sequence, and the PHLF rates were recorded. The overall mortality and PHLF rate were 0.16% and 3.0%. A total of 19 patients experienced PHLF. The numbers of patients to whom the Swiss-Clavien, Tokyo University-Makuuchi, and Chinese consensus decision trees were applied were 581, 573, and 622, and the PHLF rates were 2.75%, 2.62%, and 2.73%, respectively. Significantly more cases satisfied the Chinese consensus decision tree than the Swiss-Clavien decision tree and Tokyo University-Makuuchi decision tree (P decision trees. The Chinese consensus decision tree expands the indications for hepatic resection for HCC patients and does not increase the PHLF rate compared to the Swiss-Clavien and Tokyo University-Makuuchi decision trees. It would be a safe and effective algorithm for hepatectomy in patients with hepatocellular carcinoma.

  12. Decision and Inhibitory Trees for Decision Tables with Many-Valued Decisions

    KAUST Repository

    Azad, Mohammad

    2018-01-01

    Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set

  13. CUDT: A CUDA Based Decision Tree Algorithm

    Directory of Open Access Journals (Sweden)

    Win-Tsung Lo

    2014-01-01

    Full Text Available Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture, which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5∼55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.

  14. Multi-objective decision-making under uncertainty: Fuzzy logic methods

    Science.gov (United States)

    Hardy, Terry L.

    1995-01-01

    Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.

  15. Extended VIKOR Method for Intuitionistic Fuzzy Multiattribute Decision-Making Based on a New Distance Measure

    Directory of Open Access Journals (Sweden)

    Xiao Luo

    2017-01-01

    Full Text Available An intuitionistic fuzzy VIKOR (IF-VIKOR method is proposed based on a new distance measure considering the waver of intuitionistic fuzzy information. The method aggregates all individual decision-makers’ assessment information based on intuitionistic fuzzy weighted averaging operator (IFWA, determines the weights of decision-makers and attributes objectively using intuitionistic fuzzy entropy, calculates the group utility and individual regret by the new distance measure, and then reaches a compromise solution. It can be effectively applied to multiattribute decision-making (MADM problems where the weights of decision-makers and attributes are completely unknown and the attribute values are intuitionistic fuzzy numbers (IFNs. The validity and stability of this method are verified by example analysis and sensitivity analysis, and its superiority is illustrated by the comparison with the existing method.

  16. Advances in fuzzy decision making theory and practice

    CERN Document Server

    Skalna, Iwona; Gaweł, Bartłomiej; Basiura, Beata; Duda, Jerzy; Opiła, Janusz; Pełech-Pilichowski, Tomasz

    2015-01-01

    This book shows how common operation management methods and algorithms can be extended to deal with vague or imprecise information in decision-making problems. It describes how to combine decision trees, clustering, multi-attribute decision-making algorithms and Monte Carlo Simulation with the mathematical description of imprecise or vague information, and how to visualize such information. Moreover, it discusses a broad spectrum of real-life management problems including forecasting the apparent consumption of steel products, planning and scheduling of production processes, project portfolio selection and economic-risk estimation. It is a concise, yet comprehensive, reference source for researchers in decision-making and decision-makers in business organizations alike.

  17. Genetic Fuzzy Trees for Intelligent Control of Unmanned Combat Aerial Vehicles

    Science.gov (United States)

    Ernest, Nicholas D.

    Fuzzy Logic Control is a powerful tool that has found great success in a variety of applications. This technique relies less on complex mathematics and more "expert knowledge" of a system to bring about high-performance, resilient, and efficient control through linguistic classification of inputs and outputs and if-then rules. Genetic Fuzzy Systems (GFSs) remove the need of this expert knowledge and instead rely on a Genetic Algorithm (GA) and have similarly found great success. However, the combination of these methods suffer severely from scalability; the number of rules required to control the system increases exponentially with the number of states the inputs and outputs can take. Therefor GFSs have thus far not been applicable to complex, artificial intelligence type problems. The novel Genetic Fuzzy Tree (GFT) method breaks down complex problems hierarchically, makes sub-decisions when possible, and thus greatly reduces the burden on the GA. This development significantly changes the field of possible applications for GFSs. Within this study, this is demonstrated through applying this technique to a difficult air combat problem. Looking forward to an autonomous Unmanned Combat Aerial Vehicle (UCAV) in the 2030 time-frame, it becomes apparent that the mission, flight, and ground controls will utilize the emerging paradigm of Intelligent Systems (IS); namely, the ability to learn, adapt, exhibit robustness in uncertain situations, make sense of the data collected in real-time and extrapolate when faced with scenarios significantly different from those used in training. LETHA (Learning Enhanced Tactical Handling Algorithm) was created to develop intelligent controllers for these advanced unmanned craft as the first GFT. A simulation space referred to as HADES (Hoplological Autonomous Defend and Engage Simulation) was created in which LETHA can train the UCAVs. Equipped with advanced sensors, a limited supply of Self-Defense Missiles (SDMs), and a recharging

  18. Univariate decision tree induction using maximum margin classification

    OpenAIRE

    Yıldız, Olcay Taner

    2012-01-01

    In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree where, for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 data sets show that the novel margin tree classifier performs at least as good as C4.5 and linear discriminant tree (LDT) with a similar time complexity. F...

  19. A new web-based framework development for fuzzy multi-criteria group decision-making.

    Science.gov (United States)

    Hanine, Mohamed; Boutkhoum, Omar; Tikniouine, Abdessadek; Agouti, Tarik

    2016-01-01

    Fuzzy multi-criteria group decision making (FMCGDM) process is usually used when a group of decision-makers faces imprecise data or linguistic variables to solve the problems. However, this process contains many methods that require many time-consuming calculations depending on the number of criteria, alternatives and decision-makers in order to reach the optimal solution. In this study, a web-based FMCGDM framework that offers decision-makers a fast and reliable response service is proposed. The proposed framework includes commonly used tools for multi-criteria decision-making problems such as fuzzy Delphi, fuzzy AHP and fuzzy TOPSIS methods. The integration of these methods enables taking advantages of the strengths and complements each method's weakness. Finally, a case study of location selection for landfill waste in Morocco is performed to demonstrate how this framework can facilitate decision-making process. The results demonstrate that the proposed framework can successfully accomplish the goal of this study.

  20. Application of fuzzy fault tree analysis based on modified fuzzy AHP and fuzzy TOPSIS for fire and explosion in the process industry.

    Science.gov (United States)

    Yazdi, Mohammad; Korhan, Orhan; Daneshvar, Sahand

    2018-05-09

    This study aimed at establishing fault tree analysis (FTA) using expert opinion to compute the probability of an event. To find the probability of the top event (TE), all probabilities of the basic events (BEs) should be available when the FTA is drawn. In this case, employing expert judgment can be used as an alternative to failure data in an awkward situation. The fuzzy analytical hierarchy process as a standard technique is used to give a specific weight to each expert, and fuzzy set theory is engaged for aggregating expert opinion. In this regard, the probability of BEs will be computed and, consequently, the probability of the TE obtained using Boolean algebra. Additionally, to reduce the probability of the TE in terms of three parameters (safety consequences, cost and benefit), the importance measurement technique and modified TOPSIS was employed. The effectiveness of the proposed approach is demonstrated with a real-life case study.

  1. A New Hesitant Fuzzy Linguistic TOPSIS Method for Group Multi-Criteria Linguistic Decision Making

    Directory of Open Access Journals (Sweden)

    Fangling Ren

    2017-11-01

    Full Text Available Hesitant fuzzy linguistic decision making is a focus point in linguistic decision making, in which the main method is based on preference ordering. This paper develops a new hesitant fuzzy linguistic TOPSIS method for group multi-criteria linguistic decision making; the method is inspired by the TOPSIS method and the preference degree between two hesitant fuzzy linguistic term sets (HFLTSs. To this end, we first use the preference degree to define a pseudo-distance between two HFLTSs and analyze its properties. Then we present the positive (optimistic and negative (pessimistic information of each criterion provided by each decision maker and aggregate these by using weights of decision makers to obtain the hesitant fuzzy linguistic positive and negative ideal solutions. On the basis of the proposed pseudo-distance, we finally obtain the positive (negative ideal separation matrix and a new relative closeness degree to rank alternatives. We also design an algorithm based on the provided method to carry out hesitant fuzzy linguistic decision making. An illustrative example shows the elaboration of the proposed method and comparison with the symbolic aggregation-based method, the hesitant fuzzy linguistic TOPSIS method and the hesitant fuzzy linguistic VIKOR method; it seems that the proposed method is a useful and alternative decision-making method.

  2. The Decision Tree: A Tool for Achieving Behavioral Change.

    Science.gov (United States)

    Saren, Dru

    1999-01-01

    Presents a "Decision Tree" process for structuring team decision making and problem solving about specific student behavioral goals. The Decision Tree involves a sequence of questions/decisions that can be answered in "yes/no" terms. Questions address reasonableness of the goal, time factors, importance of the goal, responsibilities, safety,…

  3. Study of decision framework of wind farm project plan selection under intuitionistic fuzzy set and fuzzy measure environment

    International Nuclear Information System (INIS)

    Wu, Yunna; Geng, Shuai; Xu, Hu; Zhang, Haobo

    2014-01-01

    Highlights: • Experts’ opinions are expressed by using the intuitionistic fuzzy values. • Fuzzy measure is used to solve the dependence problem of criteria. • The compensatory problem of performance scores is reasonably processed. - Abstract: Project selection plays an important role in the entire life cycle of wind farm project and the multi-criteria decision making (MCDM) methods are very important in the whole wind farm project plan selection process. There are problems in the present MCDM methods decrease evaluation quality of the wind farm project plans: first, the information loss exists in the wind farm project plan evaluation process. Second, it is difficult to satisfy the independent assumption of the multi-criteria decision making methods used in the wind farm project plan evaluation in fact. Third, the compensatory problem of performance scores of the wind farm project plans is processed unreasonably. Hence the innovation points of this paper are as follows: first, the intuitionistic fuzzy numbers are used instead of fuzzy numbers or numerical values to reflect the experts’ intuitive preferences to decrease the probability of information loss; second, the fuzzy measure is used to rate the important degrees of criteria in order to avoid the independent assumption and to increase the reasonability; third, the partial compensatory problem of performance scores is well processed by using intuitionistic fuzzy Choquet (IFC) operator and generalized intuitionistic fuzzy ordered geometric averaging (GIFOGA) operator. These operators can deal with the compensatory performance scores and non-compensatory performance scores respectively. Finally, a case study demonstrates the effectiveness of decision framework

  4. Fuzzy Decision Analysis for Integrated Environmental Vulnerability Assessment of the Mid-Atlantic Region

    Science.gov (United States)

    Liem T. Tran; C. Gregory Knight; Robert V. O' Neill; Elizabeth R. Smith; Kurt H. Riitters; James D. Wickham

    2002-01-01

    A fuzzy decision analysis method for integrating ecological indicators was developed. This was a combination of a fuzzy ranking method and the analytic hierarchy process (AHP). The method was capable of ranking ecosystems in terms of environmental conditions and suggesting cumulative impacts across a large region. Using data on land cover, population, roads, streams,...

  5. A Simplified Version of the Fuzzy Decision Method and its Comparison with the Paraconsistent Decision Method

    Science.gov (United States)

    de Carvalho, Fábio Romeu; Abe, Jair Minoro

    2010-11-01

    Two recent non-classical logics have been used to make decision: fuzzy logic and paraconsistent annotated evidential logic Et. In this paper we present a simplified version of the fuzzy decision method and its comparison with the paraconsistent one. Paraconsistent annotated evidential logic Et, introduced by Da Costa, Vago and Subrahmanian (1991), is capable of handling uncertain and contradictory data without becoming trivial. It has been used in many applications such as information technology, robotics, artificial intelligence, production engineering, decision making etc. Intuitively, one Et logic formula is type p(a, b), in which a and b belong to [0, 1] (real interval) and represent respectively the degree of favorable evidence (or degree of belief) and the degree of contrary evidence (or degree of disbelief) found in p. The set of all pairs (a; b), called annotations, when plotted, form the Cartesian Unitary Square (CUS). This set, containing a similar order relation of real number, comprises a network, called lattice of the annotations. Fuzzy logic was introduced by Zadeh (1965). It tries to systematize the knowledge study, searching mainly to study the fuzzy knowledge (you don't know what it means) and distinguish it from the imprecise one (you know what it means, but you don't know its exact value). This logic is similar to paraconsistent annotated one, since it attributes a numeric value (only one, not two values) to each proposition (then we can say that it is an one-valued logic). This number translates the intensity (the degree) with which the preposition is true. Let's X a set and A, a subset of X, identified by the function f(x). For each element x∈X, you have y = f(x)∈[0, 1]. The number y is called degree of pertinence of x in A. Decision making theories based on these logics have shown to be powerful in many aspects regarding more traditional methods, like the one based on Statistics. In this paper we present a first study for a simplified

  6. Decision trees with minimum average depth for sorting eight elements

    KAUST Repository

    AbouEisha, Hassan M.

    2015-11-19

    We prove that the minimum average depth of a decision tree for sorting 8 pairwise different elements is equal to 620160/8!. We show also that each decision tree for sorting 8 elements, which has minimum average depth (the number of such trees is approximately equal to 8.548×10^326365), has also minimum depth. Both problems were considered by Knuth (1998). To obtain these results, we use tools based on extensions of dynamic programming which allow us to make sequential optimization of decision trees relative to depth and average depth, and to count the number of decision trees with minimum average depth.

  7. Shopping intention prediction using decision trees

    Directory of Open Access Journals (Sweden)

    Dario Šebalj

    2017-09-01

    Full Text Available Introduction: The price is considered to be neglected marketing mix element due to the complexity of price management and sensitivity of customers on price changes. It pulls the fastest customer reactions to that change. Accordingly, the process of making shopping decisions can be very challenging for customer. Objective: The aim of this paper is to create a model that is able to predict shopping intention and classify respondents into one of the two categories, depending on whether they intend to shop or not. Methods: Data sample consists of 305 respondents, who are persons older than 18 years involved in buying groceries for their household. The research was conducted in February 2017. In order to create a model, the decision trees method was used with its several classification algorithms. Results: All models, except the one that used RandomTree algorithm, achieved relatively high classification rate (over the 80%. The highest classification accuracy of 84.75% gave J48 and RandomForest algorithms. Since there is no statistically significant difference between those two algorithms, authors decided to choose J48 algorithm and build a decision tree. Conclusions: The value for money and price level in the store were the most significant variables for classification of shopping intention. Future study plans to compare this model with some other data mining techniques, such as neural networks or support vector machines since these techniques achieved very good accuracy in some previous research in this field.

  8. Decision trees with minimum average depth for sorting eight elements

    KAUST Repository

    AbouEisha, Hassan M.; Chikalov, Igor; Moshkov, Mikhail

    2015-01-01

    We prove that the minimum average depth of a decision tree for sorting 8 pairwise different elements is equal to 620160/8!. We show also that each decision tree for sorting 8 elements, which has minimum average depth (the number of such trees

  9. Human Factors Reliability Analysis for Assuring Nuclear Safety Using Fuzzy Fault Tree

    International Nuclear Information System (INIS)

    Eisawy, E.A.-F. I.; Sallam, H.

    2016-01-01

    In order to ensure effective prevention of harmful events, the risk assessment process cannot ignore the role of humans in the dynamics of accidental events and thus the seriousness of the consequences that may derive from them. Human reliability analysis (HRA) involves the use of qualitative and quantitative methods to assess the human contribution to risk. HRA techniques have been developed in order to provide human error probability values associated with operators’ tasks to be included within the broader context of system risk assessment, and are aimed at reducing the probability of accidental events. Fault tree analysis (FTA) is a graphical model that displays the various combinations of equipment failures and human errors that can result in the main system failure of interest. FTA is a risk analysis technique to assess likelihood (in a probabilistic context) of an event. The objective data available to estimate the likelihood is often missing, and even if available, is subject to incompleteness and imprecision or vagueness. Without addressing incompleteness and imprecision in the available data, FTA and subsequent risk analysis give a false impression of precision and correctness that undermines the overall credibility of the process. To solve this problem, qualitative justification in the context of failure possibilities can be used as alternative for quantitative justification. In this paper, we introduce the approach of fuzzy reliability as solution for fault tree analysis drawbacks. A new fuzzy fault tree method is proposed for the analysis of human reliability based on fuzzy sets and fuzzy operations t-norms, co-norms, defuzzification, and fuzzy failure probability. (author)

  10. How Uncertain Information on Service Capacity Influences the Intermodal Routing Decision: A Fuzzy Programming Perspective

    Directory of Open Access Journals (Sweden)

    Yan Sun

    2018-01-01

    Full Text Available Capacity uncertainty is a common issue in the transportation planning field. However, few studies discuss the intermodal routing problem with service capacity uncertainty. Based on our previous study on the intermodal routing under deterministic capacity consideration, we systematically explore how service capacity uncertainty influences the intermodal routing decision. First of all, we adopt trapezoidal fuzzy numbers to describe the uncertain information of the service capacity, and further transform the deterministic capacity constraint into a fuzzy chance constraint based on fuzzy credibility measure. We then integrate such fuzzy chance constraint into the mixed-integer linear programming (MILP model proposed in our previous study to develop a fuzzy chance-constrained programming model. To enable the improved model to be effectively programmed in the standard mathematical programming software and solved by exact solution algorithms, a crisp equivalent linear reformulation of the fuzzy chance constraint is generated. Finally, we modify the empirical case presented in our previous study by replacing the deterministic service capacities with trapezoidal fuzzy ones. Using the modified empirical case, we utilize sensitivity analysis and fuzzy simulation to analyze the influence of service capacity uncertainty on the intermodal routing decision, and summarize some interesting insights that are helpful for decision makers.

  11. TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees.

    Science.gov (United States)

    Muhlbacher, Thomas; Linhardt, Lorenz; Moller, Torsten; Piringer, Harald

    2018-01-01

    Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.

  12. Safety analyses of potential exposure in medical irradiation plants by Fuzzy Fault Tree

    International Nuclear Information System (INIS)

    Casamirra, Maddalena; Castiglia, Francesco; Giardina, Mariarosa; Tomarchio, Elio

    2008-01-01

    The results of Fuzzy Fault Tree (FFT) analyses of various accidental scenarios, which involve the operators in potential exposures inside an High Dose Rate (HDR) remote after-loading systems for use in brachytherapy, are reported. To carry out fault tree analyses by means of fuzzy probabilities, the TREEZZY2 computer code is used. Moreover, the HEART (Human Error Assessment and Reduction Technique) model, properly modified on the basis of the fuzzy approach, has been employed to assess the impact of performances haping and error-promoting factors in the context of the accidental events. The assessment of potential dose values for some identified accidental scenarios allows to consider, for a particular event, a fuzzy uncertainty range in potential dose estimate. The availability of lower and upper limits allows evaluating the possibility of optimization of the installation from the point of view of radiation protection. The adequacy of the training and information program for staff and patients (and their family members) and the effectiveness of behavioural rules and safety procedures were tested. Some recommendations on procedures and equipment to reduce the risk of radiological exposure are also provided. (author)

  13. Application of the fuzzy topsis multi-attribute decision making method to determine scholarship recipients

    Science.gov (United States)

    Irvanizam, I.

    2018-03-01

    Some scholarships have been routinely offered by Ministry of Research, Technology and Higher Education of the Republic of Indonesia for students at Syiah Kuala University. In reality, the scholarship selection process is becoming subjective and highly complex problem. Multi-Attribute Decision Making (MADM) techniques can be a solution in order to solve scholarship selection problem. In this study, we demonstrated the application of a fuzzy TOPSIS as an MADM technique by using a numerical example in order to calculate a triangular fuzzy number for the fuzzy data onto a normalized weight. We then use this normalized value to construct the normalized fuzzy decision matrix. We finally use the fuzzy TOPSIS to rank alternatives in descending order based on the relative closeness to the ideal solution. The result in terms of final ranking shows slightly different from the previous work.

  14. A hybrid fuzzy multi-criteria decision making model for green ...

    African Journals Online (AJOL)

    A hybrid fuzzy multi-criteria decision making model for green supplier selection. ... Hence,supplier selection is significant factor in supply chain success. ... reduce purchasing cost, lead time and improve quality and environmental issue.

  15. Fuzzy inference game approach to uncertainty in business decisions and market competitions.

    Science.gov (United States)

    Oderanti, Festus Oluseyi

    2013-01-01

    The increasing challenges and complexity of business environments are making business decisions and operations more difficult for entrepreneurs to predict the outcomes of these processes. Therefore, we developed a decision support scheme that could be used and adapted to various business decision processes. These involve decisions that are made under uncertain situations such as business competition in the market or wage negotiation within a firm. The scheme uses game strategies and fuzzy inference concepts to effectively grasp the variables in these uncertain situations. The games are played between human and fuzzy players. The accuracy of the fuzzy rule base and the game strategies help to mitigate the adverse effects that a business may suffer from these uncertain factors. We also introduced learning which enables the fuzzy player to adapt over time. We tested this scheme in different scenarios and discover that it could be an invaluable tool in the hand of entrepreneurs that are operating under uncertain and competitive business environments.

  16. A tool for study of optimal decision trees

    KAUST Repository

    Alkhalid, Abdulaziz

    2010-01-01

    The paper describes a tool which allows us for relatively small decision tables to make consecutive optimization of decision trees relative to various complexity measures such as number of nodes, average depth, and depth, and to find parameters and the number of optimal decision trees. © 2010 Springer-Verlag Berlin Heidelberg.

  17. Cloud E-Learning Service Strategies for Improving E-Learning Innovation Performance in a Fuzzy Environment by Using a New Hybrid Fuzzy Multiple Attribute Decision-Making Model

    Science.gov (United States)

    Su, Chiu Hung; Tzeng, Gwo-Hshiung; Hu, Shu-Kung

    2016-01-01

    The purpose of this study was to address this problem by applying a new hybrid fuzzy multiple criteria decision-making model including (a) using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique to construct the fuzzy scope influential network relationship map (FSINRM) and determine the fuzzy influential weights of the…

  18. Some applications of fuzzy sets and the analytical hierarchy process to decision making

    OpenAIRE

    Castro, Alberto Rosas

    1984-01-01

    Approved for public release; distribution unlimited This thesis examines the use of fuzzy set theory and the analytic hierarchy process in decision making. It begins by reviewing the insight of psychologists, social scientists and computer scientists to the decision making process. The Operations Research- Systems Analysis approach is discussed followed by a presentation of the basis of fuzzy set theory and the analytic hierarchy process. Two applications of these meth...

  19. Penyelesaian Persoalan Transportasi Fuzzy Cost Menggunakan Pendekatan Basis Tree dan Metode Nwc-stepping Stone

    OpenAIRE

    Ari, Maxsi; Herman, Asep

    2013-01-01

    The issue of transportation is a linier program. Practically, variables of transportation problems may vary that emerges more than single. Fuzzy cost idea in transportation problems is required. The application of basis tree approach and NWC-Stepping Stone method is used to determine a minimum cost of purchase and delivery of commodity occurred for some suppliers. The company can do a cost-saving and be more effective in determining goods purchase from the supplier. The method used is quanti...

  20. The fuzzy set theory application to the analysis of accident progression event trees with phenomenological uncertainty issues

    International Nuclear Information System (INIS)

    Chun, Moon-Hyun; Ahn, Kwang-Il

    1991-01-01

    Fuzzy set theory provides a formal framework for dealing with the imprecision and vagueness inherent in the expert judgement, and therefore it can be used for more effective analysis of accident progression of PRA where experts opinion is a major means for quantifying some event probabilities and uncertainties. In this paper, an example application of the fuzzy set theory is first made to a simple portion of a given accident progression event tree with typical qualitative fuzzy input data, and thereby computational algorithms suitable for application of the fuzzy set theory to the accident progression event tree analysis are identified and illustrated with example applications. Then the procedure used in the simple example is extended to extremely complex accident progression event trees with a number of phenomenological uncertainty issues, i.e., a typical plant damage state 'SEC' of the Zion Nuclear Power Plant risk assessment. The results show that the fuzzy averages of the fuzzy outcomes are very close to the mean values obtained by current methods. The main purpose of this paper is to provide a formal procedure for application of the fuzzy set theory to accident progression event trees with imprecise and qualitative branch probabilities and/or with a number of phenomenological uncertainty issues. (author)

  1. Dual hesitant pythagorean fuzzy Hamacher aggregation operators in multiple attribute decision making

    Directory of Open Access Journals (Sweden)

    Wei Guiwu

    2017-09-01

    Full Text Available In this paper, we investigate the multiple attribute decision making (MADM problem based on the Hamacher aggregation operators with dual Pythagorean hesitant fuzzy information. Then, motivated by the ideal of Hamacher operation, we have developed some Hamacher aggregation operators for aggregating dual hesitant Pythagorean fuzzy information. The prominent characteristic of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the dual hesitant Pythagorean fuzzy multiple attribute decision making problems. Finally, a practical example for supplier selection in supply chain management is given to verify the developed approach and to demonstrate its practicality and effectiveness.

  2. Stock Picking via Nonsymmetrically Pruned Binary Decision Trees

    OpenAIRE

    Anton Andriyashin

    2008-01-01

    Stock picking is the field of financial analysis that is of particular interest for many professional investors and researchers. In this study stock picking is implemented via binary classification trees. Optimal tree size is believed to be the crucial factor in forecasting performance of the trees. While there exists a standard method of tree pruning, which is based on the cost-complexity tradeoff and used in the majority of studies employing binary decision trees, this paper introduces a no...

  3. On algorithm for building of optimal α-decision trees

    KAUST Repository

    Alkhalid, Abdulaziz

    2010-01-01

    The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.

  4. Automatic design of decision-tree induction algorithms

    CERN Document Server

    Barros, Rodrigo C; Freitas, Alex A

    2015-01-01

    Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning, and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain o

  5. Comparison of greedy algorithms for α-decision tree construction

    KAUST Repository

    Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail

    2011-01-01

    A comparison among different heuristics that are used by greedy algorithms which constructs approximate decision trees (α-decision trees) is presented. The comparison is conducted using decision tables based on 24 data sets from UCI Machine Learning Repository [2]. Complexity of decision trees is estimated relative to several cost functions: depth, average depth, number of nodes, number of nonterminal nodes, and number of terminal nodes. Costs of trees built by greedy algorithms are compared with minimum costs calculated by an algorithm based on dynamic programming. The results of experiments assign to each cost function a set of potentially good heuristics that minimize it. © 2011 Springer-Verlag.

  6. The coordinating contracts of supply chain in a fuzzy decision environment.

    Science.gov (United States)

    Sang, Shengju

    2016-01-01

    The rapid change of the product life cycle is making the parameters of the supply chain models more and more uncertain. Therefore, we consider the coordination mechanisms between one manufacturer and one retailer in a fuzzy decision marking environment, where the parameters of the models can be forecasted and expressed as the triangular fuzzy variables. The centralized decision-making system, two types of supply chain contracts, namely, the revenue sharing contract and the return contract are proposed. To obtain their optimal policies, the fuzzy set theory is adopted to solve these fuzzy models. Finally, three numerical examples are provided to analyze the impacts of the fuzziness of the market demand, retail price and salvage value of the product on the optimal solutions in two contracts. It shows that in order to obtain more fuzzy expected profits the retailer and the manufacturer should seek as low fuzziness of demand, high fuzziness of the retail price and the salvage value as possible in both contracts.

  7. Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions

    KAUST Repository

    Azad, Mohammad

    2017-06-16

    We study problems of optimization of decision and inhibitory trees for decision tables with many-valued decisions. As cost functions, we consider depth, average depth, number of nodes, and number of terminal/nonterminal nodes in trees. Decision tables with many-valued decisions (multi-label decision tables) are often more accurate models for real-life data sets than usual decision tables with single-valued decisions. Inhibitory trees can sometimes capture more information from decision tables than decision trees. In this paper, we create dynamic programming algorithms for multi-stage optimization of trees relative to a sequence of cost functions. We apply these algorithms to prove the existence of totally optimal (simultaneously optimal relative to a number of cost functions) decision and inhibitory trees for some modified decision tables from the UCI Machine Learning Repository.

  8. Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions

    KAUST Repository

    Azad, Mohammad; Moshkov, Mikhail

    2017-01-01

    We study problems of optimization of decision and inhibitory trees for decision tables with many-valued decisions. As cost functions, we consider depth, average depth, number of nodes, and number of terminal/nonterminal nodes in trees. Decision tables with many-valued decisions (multi-label decision tables) are often more accurate models for real-life data sets than usual decision tables with single-valued decisions. Inhibitory trees can sometimes capture more information from decision tables than decision trees. In this paper, we create dynamic programming algorithms for multi-stage optimization of trees relative to a sequence of cost functions. We apply these algorithms to prove the existence of totally optimal (simultaneously optimal relative to a number of cost functions) decision and inhibitory trees for some modified decision tables from the UCI Machine Learning Repository.

  9. Putting the use of intuition for fuzzy front end decision making on the research agenda

    NARCIS (Netherlands)

    Eling, K.; Langerak, F.

    2011-01-01

    Decision making literature suggests that intuitive decision making is more appropriate than the established rational decision making approaches to handle the specific information processing needs of the fuzzy front end (FFE) of new product development. However, these earlier studies cannot be

  10. An approach to decision-making with triangular fuzzy reciprocal preference relations and its application

    Science.gov (United States)

    Meng, Fanyong

    2018-02-01

    Triangular fuzzy reciprocal preference relations (TFRPRs) are powerful tools to denoting decision-makers' fuzzy judgments, which permit the decision-makers to apply triangular fuzzy ratio rather than real numbers to express their judgements. Consistency analysis is one of the most crucial issues in preference relations that can guarantee the reasonable ranking order. However, all previous consistency concepts cannot well address this type of preference relations. Based on the operational laws on triangular fuzzy numbers, this paper introduces an additive consistency concept for TFRPRs by using quasi TFRPRs, which can be seen as a natural extension of the crisp case. Using this consistency concept, models to judging the additive consistency of TFRPRs and to estimating missing values in complete TFRPRs are constructed. Then, an algorithm to decision-making with TFRPRs is developed. Finally, two numerical examples are offered to illustrate the application of the proposed procedure, and comparison analysis is performed.

  11. Correlation Coefficients of Extended Hesitant Fuzzy Sets and Their Applications to Decision Making

    Directory of Open Access Journals (Sweden)

    Na Lu

    2017-03-01

    Full Text Available Extended hesitant fuzzy sets (EHFSs, which allow the membership degree of an element to a set represented by several possible value-groups, can be considered as a powerful tool to express uncertain information in the process of group decision making. Therefore, we derive some correlation coefficients between EHFSs, which contain two cases, the correlation coefficients taking into account the length of extended hesitant fuzzy elements (EHFEs and the correlation coefficients without taking into account the length of EHFEs, as a new extension of existing correlation coefficients for hesitant fuzzy sets (HFSs and apply them to decision making under extended hesitant fuzzy environments. A real-world example based on the energy policy problem is employed to illustrate the actual need for dealing with the difference of evaluation information provided by different experts without information loss in decision making processes.

  12. Use of a fuzzy decision-making method in evaluating severe accident management strategies

    International Nuclear Information System (INIS)

    Jae, M.; Moon, J.H.

    2002-01-01

    In developing severe accident management strategies, an engineering decision would be made based on the available data and information that are vague, imprecise and uncertain by nature. These sorts of vagueness and uncertainty are due to lack of knowledge for the severe accident sequences of interest. The fuzzy set theory offers a possibility of handling these sorts of data and information. In this paper, the possibility to apply the decision-making method based on fuzzy set theory to the evaluation of the accident management strategies at a nuclear power plant is scrutinized. The fuzzy decision-making method uses linguistic variables and fuzzy numbers to represent the decision-maker's subjective assessments for the decision alternatives according to the decision criteria. The fuzzy mean operator is used to aggregate the decision-maker's subjective assessments, while the total integral value method is used to rank the decision alternatives. As a case study, the proposed method is applied to evaluating the accident management strategies at a nuclear power plant

  13. Hybrid Multicriteria Group Decision Making Method for Information System Project Selection Based on Intuitionistic Fuzzy Theory

    Directory of Open Access Journals (Sweden)

    Jian Guo

    2013-01-01

    Full Text Available Information system (IS project selection is of critical importance to every organization in dynamic competing environment. The aim of this paper is to develop a hybrid multicriteria group decision making approach based on intuitionistic fuzzy theory for IS project selection. The decision makers’ assessment information can be expressed in the form of real numbers, interval-valued numbers, linguistic variables, and intuitionistic fuzzy numbers (IFNs. All these evaluation pieces of information can be transformed to the form of IFNs. Intuitionistic fuzzy weighted averaging (IFWA operator is utilized to aggregate individual opinions of decision makers into a group opinion. Intuitionistic fuzzy entropy is used to obtain the entropy weights of the criteria. TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate IS project in group decision making environment. Finally, a numerical example for information system projects selection is given to illustrate application of hybrid multi-criteria group decision making (MCGDM method based on intuitionistic fuzzy theory and TOPSIS method.

  14. A tool for study of optimal decision trees

    KAUST Repository

    Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail

    2010-01-01

    The paper describes a tool which allows us for relatively small decision tables to make consecutive optimization of decision trees relative to various complexity measures such as number of nodes, average depth, and depth, and to find parameters

  15. Fuzzy methods in decision making process - A particular approach in manufacturing systems

    Science.gov (United States)

    Coroiu, A. M.

    2015-11-01

    We are living in a competitive environment, so we can see and understand that the most of manufacturing firms do the best in order to accomplish meeting demand, increasing quality, decreasing costs, and delivery rate. In present a stake point of interest is represented by the development of fuzzy technology. A particular approach for this is represented through the development of methodologies to enhance the ability to managed complicated optimization and decision making aspects involving non-probabilistic uncertainty with the reason to understand, development, and practice the fuzzy technologies to be used in fields such as economic, engineering, management, and societal problems. Fuzzy analysis represents a method for solving problems which are related to uncertainty and vagueness; it is used in multiple areas, such as engineering and has applications in decision making problems, planning and production. As a definition for decision making process we can use the next one: result of mental processes based upon cognitive process with a main role in the selection of a course of action among several alternatives. Every process of decision making can be represented as a result of a final choice and the output can be represented as an action or as an opinion of choice. Different types of uncertainty can be discovered in a wide variety of optimization and decision making problems related to planning and operation of power systems and subsystems. The mixture of the uncertainty factor in the construction of different models serves for increasing their adequacy and, as a result, the reliability and factual efficiency of decisions based on their analysis. Another definition of decision making process which came to illustrate and sustain the necessity of using fuzzy method: the decision making is an approach of choosing a strategy among many different projects in order to achieve some purposes and is formulated as three different models: high risk decision, usual risk

  16. Combining Fuzzy AHP with GIS and Decision Rules for Industrial Site Selection

    Directory of Open Access Journals (Sweden)

    Aissa Taibi

    2017-12-01

    Full Text Available This study combines Fuzzy Analytic Hierarchy Process (FAHP, Geographic Information System (GIS and Decision rules to provide decision makers with a ranking model for industrial sites in Algeria. A ranking of the suitable industrial areas is a crucial multi-criteria decision problem based on socio-economical and technical criteria as on environmental considerations. Fuzzy AHP is used for assessment of the candidate industrial sites by combining fuzzy set theory and analytic hierarchy process (AHP. The decision rule base serves as a filter that performs criteria pre-treatment involving a reduction of their numbers. GIS is used to overlay, generate criteria maps and for visualizing ranked zones on the map. The rank of a zone so obtained is an index that guides decision-makers to the best utilization of the zone in future.

  17. Relationships for Cost and Uncertainty of Decision Trees

    KAUST Repository

    Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2013-01-01

    This chapter is devoted to the design of new tools for the study of decision trees. These tools are based on dynamic programming approach and need the consideration of subtables of the initial decision table. So this approach is applicable only to relatively small decision tables. The considered tools allow us to compute: 1. Theminimum cost of an approximate decision tree for a given uncertainty value and a cost function. 2. The minimum number of nodes in an exact decision tree whose depth is at most a given value. For the first tool we considered various cost functions such as: depth and average depth of a decision tree and number of nodes (and number of terminal and nonterminal nodes) of a decision tree. The uncertainty of a decision table is equal to the number of unordered pairs of rows with different decisions. The uncertainty of approximate decision tree is equal to the maximum uncertainty of a subtable corresponding to a terminal node of the tree. In addition to the algorithms for such tools we also present experimental results applied to various datasets acquired from UCI ML Repository [4]. © Springer-Verlag Berlin Heidelberg 2013.

  18. Group decision-making approach for flood vulnerability identification using the fuzzy VIKOR method

    Science.gov (United States)

    Lee, G.; Jun, K. S.; Chung, E.-S.

    2015-04-01

    This study proposes an improved group decision making (GDM) framework that combines the VIKOR method with data fuzzification to quantify the spatial flood vulnerability including multiple criteria. In general, GDM method is an effective tool for formulating a compromise solution that involves various decision makers since various stakeholders may have different perspectives on their flood risk/vulnerability management responses. The GDM approach is designed to achieve consensus building that reflects the viewpoints of each participant. The fuzzy VIKOR method was developed to solve multi-criteria decision making (MCDM) problems with conflicting and noncommensurable criteria. This comprising method can be used to obtain a nearly ideal solution according to all established criteria. This approach effectively can propose some compromising decisions by combining the GDM method and fuzzy VIKOR method. The spatial flood vulnerability of the southern Han River using the GDM approach combined with the fuzzy VIKOR method was compared with the spatial flood vulnerability using general MCDM methods, such as the fuzzy TOPSIS and classical GDM methods (i.e., Borda, Condorcet, and Copeland). As a result, the proposed fuzzy GDM approach can reduce the uncertainty in the data confidence and weight derivation techniques. Thus, the combination of the GDM approach with the fuzzy VIKOR method can provide robust prioritization because it actively reflects the opinions of various groups and considers uncertainty in the input data.

  19. Pythagorean Fuzzy Muirhead Mean Operators and Their Application in Multiple-Criteria Group Decision-Making

    Directory of Open Access Journals (Sweden)

    Jianghong Zhu

    2018-06-01

    Full Text Available As a generalization of the intuitionistic fuzzy set (IFS, a Pythagorean fuzzy set has more flexibility than IFS in expressing uncertainty and fuzziness in the process of multiple criteria group decision-making (MCGDM. Meanwhile, the prominent advantage of the Muirhead mean (MM operator is that it can reflect the relationships among the various input arguments through changing a parameter vector. Motivated by these primary characters, in this study, we introduced the MM operator into the Pythagorean fuzzy context to expand its applied fields. To do so, we presented the Pythagorean fuzzy MM (PFMM operators and Pythagorean fuzzy dual MM (PFDMM operator to fuse the Pythagorean fuzzy information. Then, we investigated their some properties and gave some special cases related to the parameter vector. In addition, based on the developed operators, two MCGDM methods under the Pythagorean fuzzy environment are proposed. An example is given to verify the validity and feasibility of our proposed methods, and a comparative analysis is provided to show their advantages.

  20. 15 CFR Supplement 1 to Part 732 - Decision Tree

    Science.gov (United States)

    2010-01-01

    ... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

  1. Greedy algorithm with weights for decision tree construction

    KAUST Repository

    Moshkov, Mikhail

    2010-12-01

    An approximate algorithm for minimization of weighted depth of decision trees is considered. A bound on accuracy of this algorithm is obtained which is unimprovable in general case. Under some natural assumptions on the class NP, the considered algorithm is close (from the point of view of accuracy) to best polynomial approximate algorithms for minimization of weighted depth of decision trees.

  2. Greedy algorithm with weights for decision tree construction

    KAUST Repository

    Moshkov, Mikhail

    2010-01-01

    An approximate algorithm for minimization of weighted depth of decision trees is considered. A bound on accuracy of this algorithm is obtained which is unimprovable in general case. Under some natural assumptions on the class NP, the considered algorithm is close (from the point of view of accuracy) to best polynomial approximate algorithms for minimization of weighted depth of decision trees.

  3. Decision-Tree Formulation With Order-1 Lateral Execution

    Science.gov (United States)

    James, Mark

    2007-01-01

    A compact symbolic formulation enables mapping of an arbitrarily complex decision tree of a certain type into a highly computationally efficient multidimensional software object. The type of decision trees to which this formulation applies is that known in the art as the Boolean class of balanced decision trees. Parallel lateral slices of an object created by means of this formulation can be executed in constant time considerably less time than would otherwise be required. Decision trees of various forms are incorporated into almost all large software systems. A decision tree is a way of hierarchically solving a problem, proceeding through a set of true/false responses to a conclusion. By definition, a decision tree has a tree-like structure, wherein each internal node denotes a test on an attribute, each branch from an internal node represents an outcome of a test, and leaf nodes represent classes or class distributions that, in turn represent possible conclusions. The drawback of decision trees is that execution of them can be computationally expensive (and, hence, time-consuming) because each non-leaf node must be examined to determine whether to progress deeper into a tree structure or to examine an alternative. The present formulation was conceived as an efficient means of representing a decision tree and executing it in as little time as possible. The formulation involves the use of a set of symbolic algorithms to transform a decision tree into a multi-dimensional object, the rank of which equals the number of lateral non-leaf nodes. The tree can then be executed in constant time by means of an order-one table lookup. The sequence of operations performed by the algorithms is summarized as follows: 1. Determination of whether the tree under consideration can be encoded by means of this formulation. 2. Extraction of decision variables. 3. Symbolic optimization of the decision tree to minimize its form. 4. Expansion and transformation of all nested conjunctive

  4. Relationships among various parameters for decision tree optimization

    KAUST Repository

    Hussain, Shahid

    2014-01-14

    In this chapter, we study, in detail, the relationships between various pairs of cost functions and between uncertainty measure and cost functions, for decision tree optimization. We provide new tools (algorithms) to compute relationship functions, as well as provide experimental results on decision tables acquired from UCI ML Repository. The algorithms presented in this paper have already been implemented and are now a part of Dagger, which is a software system for construction/optimization of decision trees and decision rules. The main results presented in this chapter deal with two types of algorithms for computing relationships; first, we discuss the case where we construct approximate decision trees and are interested in relationships between certain cost function, such as depth or number of nodes of a decision trees, and an uncertainty measure, such as misclassification error (accuracy) of decision tree. Secondly, relationships between two different cost functions are discussed, for example, the number of misclassification of a decision tree versus number of nodes in a decision trees. The results of experiments, presented in the chapter, provide further insight. © 2014 Springer International Publishing Switzerland.

  5. Relationships among various parameters for decision tree optimization

    KAUST Repository

    Hussain, Shahid

    2014-01-01

    In this chapter, we study, in detail, the relationships between various pairs of cost functions and between uncertainty measure and cost functions, for decision tree optimization. We provide new tools (algorithms) to compute relationship functions, as well as provide experimental results on decision tables acquired from UCI ML Repository. The algorithms presented in this paper have already been implemented and are now a part of Dagger, which is a software system for construction/optimization of decision trees and decision rules. The main results presented in this chapter deal with two types of algorithms for computing relationships; first, we discuss the case where we construct approximate decision trees and are interested in relationships between certain cost function, such as depth or number of nodes of a decision trees, and an uncertainty measure, such as misclassification error (accuracy) of decision tree. Secondly, relationships between two different cost functions are discussed, for example, the number of misclassification of a decision tree versus number of nodes in a decision trees. The results of experiments, presented in the chapter, provide further insight. © 2014 Springer International Publishing Switzerland.

  6. Hesitant Fuzzy Thermodynamic Method for Emergency Decision Making Based on Prospect Theory.

    Science.gov (United States)

    Ren, Peijia; Xu, Zeshui; Hao, Zhinan

    2017-09-01

    Due to the timeliness of emergency response and much unknown information in emergency situations, this paper proposes a method to deal with the emergency decision making, which can comprehensively reflect the emergency decision making process. By utilizing the hesitant fuzzy elements to represent the fuzziness of the objects and the hesitant thought of the experts, this paper introduces the negative exponential function into the prospect theory so as to portray the psychological behaviors of the experts, which transforms the hesitant fuzzy decision matrix into the hesitant fuzzy prospect decision matrix (HFPDM) according to the expectation-levels. Then, this paper applies the energy and the entropy in thermodynamics to take the quantity and the quality of the decision values into account, and defines the thermodynamic decision making parameters based on the HFPDM. Accordingly, a whole procedure for emergency decision making is conducted. What is more, some experiments are designed to demonstrate and improve the validation of the emergency decision making procedure. Last but not the least, this paper makes a case study about the emergency decision making in the firing and exploding at Port Group in Tianjin Binhai New Area, which manifests the effectiveness and practicability of the proposed method.

  7. A Fuzzy Max–Min Decision Bi-Level Fuzzy Programming Model for Water Resources Optimization Allocation under Uncertainty

    Directory of Open Access Journals (Sweden)

    Chongfeng Ren

    2018-04-01

    Full Text Available Water competing conflict among water competing sectors from different levels should be taken under consideration during the optimization allocation of water resources. Furthermore, uncertainties are inevitable in the optimization allocation of water resources. In order to deal with the above problems, this study developed a fuzzy max–min decision bi-level fuzzy programming model. The developed model was then applied to a case study in Wuwei, Gansu Province, China. In this study, the net benefit and yield were regarded as the upper-level and lower-level objectives, respectively. Optimal water resource plans were obtained under different possibility levels of fuzzy parameters, which could deal with water competing conflict between the upper level and the lower level effectively. The obtained results are expected to make great contribution in helping local decision-makers to make decisions on dealing with the water competing conflict between the upper and lower level and the optimal use of water resources under uncertainty.

  8. Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support

    OpenAIRE

    S. Parkash  Kumar; K. S. Ramaswami

    2011-01-01

    Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using...

  9. Estimation of power lithium-ion battery SOC based on fuzzy optimal decision

    Science.gov (United States)

    He, Dongmei; Hou, Enguang; Qiao, Xin; Liu, Guangmin

    2018-06-01

    In order to improve vehicle performance and safety, need to accurately estimate the power lithium battery state of charge (SOC), analyzing the common SOC estimation methods, according to the characteristics open circuit voltage and Kalman filter algorithm, using T - S fuzzy model, established a lithium battery SOC estimation method based on the fuzzy optimal decision. Simulation results show that the battery model accuracy can be improved.

  10. Determining rules for closing customer service centers: A public utility company's fuzzy decision

    Science.gov (United States)

    Dekorvin, Andre; Shipley, Margaret F.; Lea, Robert N.

    1992-01-01

    In the present work, we consider the general problem of knowledge acquisition under uncertainty. Simply stated, the problem reduces to the following: how can we capture the knowledge of an expert when the expert is unable to clearly formulate how he or she arrives at a decision? A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision may have been made. Unique to our work is the fuzzy set representation of the conditions or attributes upon which the expert may possibly base his fuzzy decision. From our examples, we infer certain and possible fuzzy rules for closing a customer service center and illustrate the importance of having the decision closely relate to the conditions under consideration.

  11. Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia

    Science.gov (United States)

    Abdullah, Lazim; Najib, Liana

    2016-04-01

    Energy consumption for developing countries is sharply increasing due to the higher economic growth due to industrialisation along with population growth and urbanisation. The increasing demand of energy leads to global energy crisis. Selecting the best energy technology and conservation requires both quantitative and qualitative evaluation criteria. The fuzzy set-based approach is one of the well-known theories to handle fuzziness, uncertainty in decision-making and vagueness of information. This paper proposes a new method of intuitionistic fuzzy analytic hierarchy process (IF-AHP) to deal with the uncertainty in decision-making. The new IF-AHP is applied to establish a preference in the sustainable energy planning decision-making problem. Three decision-makers attached with Malaysian government agencies were interviewed to provide linguistic judgement prior to analysing with the new IF-AHP. Nuclear energy has been decided as the best alternative in energy planning which provides the highest weight among all the seven alternatives.

  12. Fuzzy Neural Networks for Decision Support in Negotiation

    International Nuclear Information System (INIS)

    Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

    2008-01-01

    There is a large number of parameters which one can take into account when building a negotiation model. These parameters in general are uncertain, thus leading to models which represents them with fuzzy sets. On the other hand, the nature of these parameters makes them very difficult to model them with precise values. During negotiation, these parameters play an important role by altering the outcomes or changing the state of the negotiators. One reasonable way to model this procedure is to accept fuzzy relations (from theory or experience). The action of these relations to fuzzy sets, produce new fuzzy sets which describe now the new state of the system or the modified parameters. But, in the majority of these situations, the relations are multidimensional, leading to complicated models and exponentially increasing computational time. In this paper a solution to this problem is presented. The use of fuzzy neural networks is shown that it can substitute the use of fuzzy relations with comparable results. Finally a simple simulation is carried in order to test the new method.

  13. An automated approach to the design of decision tree classifiers

    Science.gov (United States)

    Argentiero, P.; Chin, R.; Beaudet, P.

    1982-01-01

    An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

  14. Minimization of decision tree depth for multi-label decision tables

    KAUST Repository

    Azad, Mohammad

    2014-10-01

    In this paper, we consider multi-label decision tables that have a set of decisions attached to each row. Our goal is to find one decision from the set of decisions for each row by using decision tree as our tool. Considering our target to minimize the depth of the decision tree, we devised various kinds of greedy algorithms as well as dynamic programming algorithm. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of depth of decision trees.

  15. Construction of α-decision trees for tables with many-valued decisions

    KAUST Repository

    Moshkov, Mikhail; Zielosko, Beata

    2011-01-01

    The paper is devoted to the study of greedy algorithm for construction of approximate decision trees (α-decision trees). This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. We consider bound on the number of algorithm steps, and bound on the algorithm accuracy relative to the depth of decision trees. © 2011 Springer-Verlag.

  16. Minimization of decision tree depth for multi-label decision tables

    KAUST Repository

    Azad, Mohammad; Moshkov, Mikhail

    2014-01-01

    In this paper, we consider multi-label decision tables that have a set of decisions attached to each row. Our goal is to find one decision from the set of decisions for each row by using decision tree as our tool. Considering our target to minimize the depth of the decision tree, we devised various kinds of greedy algorithms as well as dynamic programming algorithm. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of depth of decision trees.

  17. Using intuition in fuzzy front-end decision-making : a conceptual framework

    NARCIS (Netherlands)

    Eling, K.; Griffin, A.; Langerak, F.

    2014-01-01

    The goal of decision-making during the execution of the fuzzy front end (FFE) is to develop a creative new product concept. Although intuitive decision-making has been found to increase new product creativity, the theoretical knowledge base as to why and under which conditions intuition use during

  18. Identifying Bank Frauds Using CRISP-DM and Decision Trees

    OpenAIRE

    Bruno Carneiro da Rocha; Rafael Timóteo de Sousa Júnior

    2010-01-01

    This article aims to evaluate the use of techniques of decision trees, in conjunction with the managementmodel CRISP-DM, to help in the prevention of bank fraud. This article offers a study on decision trees, animportant concept in the field of artificial intelligence. The study is focused on discussing how these treesare able to assist in the decision making process of identifying frauds by the analysis of informationregarding bank transactions. This information is captured with the use of t...

  19. Decision tree methods: applications for classification and prediction.

    Science.gov (United States)

    Song, Yan-Yan; Lu, Ying

    2015-04-25

    Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.

  20. Bi-Criteria Optimization of Decision Trees with Applications to Data Analysis

    KAUST Repository

    Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2017-01-01

    : the study of relationships among depth, average depth and number of nodes for decision trees for corner point detection (such trees are used in computer vision for object tracking), study of systems of decision rules derived from decision trees

  1. HOSPITAL SITE SELECTION USING TWO-STAGE FUZZY MULTI-CRITERIA DECISION MAKING PROCESS

    Directory of Open Access Journals (Sweden)

    Ali Soltani

    2011-06-01

    Full Text Available Site selection for sitting of urban activities/facilities is one of the crucial policy-related decisions taken by urban planners and policy makers. The process of site selection is inherently complicated. A careless site imposes exorbitant costs on city budget and damages the environment inevitably. Nowadays, multi-attributes decision making approaches are suggested to use to improve precision of decision making and reduce surplus side effects. Two well-known techniques, analytical hierarchal process and analytical network process are among multi-criteria decision making systems which can easily be consistent with both quantitative and qualitative criteria. These are also developed to be fuzzy analytical hierarchal process and fuzzy analytical network process systems which are capable of accommodating inherent uncertainty and vagueness in multi-criteria decision-making. This paper reports the process and results of a hospital site selection within the Region 5 of Shiraz metropolitan area, Iran using integrated fuzzy analytical network process systems with Geographic Information System (GIS. The weights of the alternatives were calculated using fuzzy analytical network process. Then a sensitivity analysis was conducted to measure the elasticity of a decision in regards to different criteria. This study contributes to planning practice by suggesting a more comprehensive decision making tool for site selection.

  2. HOSPITAL SITE SELECTION USING TWO-STAGE FUZZY MULTI-CRITERIA DECISION MAKING PROCESS

    Directory of Open Access Journals (Sweden)

    Ali Soltani

    2011-01-01

    Full Text Available Site selection for sitting of urban activities/facilities is one of the crucial policy-related decisions taken by urban planners and policy makers. The process of site selection is inherently complicated. A careless site imposes exorbitant costs on city budget and damages the environment inevitably. Nowadays, multi-attributes decision making approaches are suggested to use to improve precision of decision making and reduce surplus side effects. Two well-known techniques, analytical hierarchal process and analytical network process are among multi-criteria decision making systems which can easily be consistent with both quantitative and qualitative criteria. These are also developed to be fuzzy analytical hierarchal process and fuzzy analytical network process systems which are capable of accommodating inherent uncertainty and vagueness in multi-criteria decision-making. This paper reports the process and results of a hospital site selection within the Region 5 of Shiraz metropolitan area, Iran using integrated fuzzy analytical network process systems with Geographic Information System (GIS. The weights of the alternatives were calculated using fuzzy analytical network process. Then a sensitivity analysis was conducted to measure the elasticity of a decision in regards to different criteria. This study contributes to planning practice by suggesting a more comprehensive decision making tool for site selection.

  3. The Interval-Valued Triangular Fuzzy Soft Set and Its Method of Dynamic Decision Making

    Directory of Open Access Journals (Sweden)

    Xiaoguo Chen

    2014-01-01

    Full Text Available A concept of interval-valued triangular fuzzy soft set is presented, and some operations of “AND,” “OR,” intersection, union and complement, and so forth are defined. Then some relative properties are discussed and several conclusions are drawn. A dynamic decision making model is built based on the definition of interval-valued triangular fuzzy soft set, in which period weight is determined by the exponential decay method. The arithmetic weighted average operator of interval-valued triangular fuzzy soft set is given by the aggregating thought, thereby aggregating interval-valued triangular fuzzy soft sets of different time-series into a collective interval-valued triangular fuzzy soft set. The formulas of selection and decision values of different objects are given; therefore the optimal decision making is achieved according to the decision values. Finally, the steps of this method are concluded, and one example is given to explain the application of the method.

  4. Intrathoracic Airway Tree Segmentation from CT Images Using a Fuzzy Connectivity Method

    Directory of Open Access Journals (Sweden)

    Fereshteh Yousefi Rizi

    2009-03-01

    Full Text Available Introduction: Virtual bronchoscopy is a reliable and efficient diagnostic method for primary symptoms of lung cancer. The segmentation of airways from CT images is a critical step for numerous virtual bronchoscopy applications. Materials and Methods: To overcome the limitations of the fuzzy connectedness method, the proposed technique, called fuzzy connectivity - fuzzy C-mean (FC-FCM, utilized the FCM algorithm. Then, hanging-togetherness of pixels was handled by employing a spatial membership function. Another problem in airway segmentation that had to be overcome was the leakage into the extra-luminal regions due to the thinness of the airway walls during the process of segmentation. Results:   The result shows an accuracy of 92.92% obtained for segmentation of the airway tree up to the fourth generation. Conclusion:  We have presented a new segmentation method that is not only robust regarding the leakage problem but also functions more efficiently than the traditional FC method.

  5. Fuzzy logics acquisition and simulation modules for expert systems to assist operator's decision for nuclear power stations

    International Nuclear Information System (INIS)

    Averkin, A.A.

    1994-01-01

    A new type of fuzzy expert system for assisting the operator's decisions in nuclear power plant system in non-standard situations is proposed. This expert system is based on new approaches to fuzzy logics acquisition and to fuzzy logics testing. Fuzzy logics can be generated by a T-norms axiomatic system to choose the most suitable to operator's way of thinking. Then the chosen fuzzy logic is tested by simulation of inference process in expert system. The designed logic is the input of inference module of expert system

  6. Evaluation of Cloud Services: A Fuzzy Multi-Criteria Group Decision Making Method

    Directory of Open Access Journals (Sweden)

    Santoso Wibowo

    2016-12-01

    Full Text Available This paper presents a fuzzy multi-criteria group decision making method for evaluating the performance of Cloud services in an uncertain environment. Intuitionistic fuzzy numbers are used to better model the subjectivity and imprecision in the performance evaluation process. An effective algorithm is developed based on the technique for order preference by similarity to the ideal solution and the Choquet integral operator for adequately solving the performance evaluation problem. An example is presented for demonstrating the applicability of the proposed method for solving the multi-criteria group decision making problem in real situations.

  7. Pythagorean fuzzy analytic hierarchy process to multi-criteria decision making

    Science.gov (United States)

    Mohd, Wan Rosanisah Wan; Abdullah, Lazim

    2017-11-01

    A numerous approaches have been proposed in the literature to determine the criteria of weight. The weight of criteria is very significant in the process of decision making. One of the outstanding approaches that used to determine weight of criteria is analytic hierarchy process (AHP). This method involves decision makers (DMs) to evaluate the decision to form the pair-wise comparison between criteria and alternatives. In classical AHP, the linguistic variable of pairwise comparison is presented in terms of crisp value. However, this method is not appropriate to present the real situation of the problems because it involved the uncertainty in linguistic judgment. For this reason, AHP has been extended by incorporating the Pythagorean fuzzy sets. In addition, no one has found in the literature proposed how to determine the weight of criteria using AHP under Pythagorean fuzzy sets. In order to solve the MCDM problem, the Pythagorean fuzzy analytic hierarchy process is proposed to determine the criteria weight of the evaluation criteria. Using the linguistic variables, pairwise comparison for evaluation criteria are made to the weights of criteria using Pythagorean fuzzy numbers (PFNs). The proposed method is implemented in the evaluation problem in order to demonstrate its applicability. This study shows that the proposed method provides us with a useful way and a new direction in solving MCDM problems with Pythagorean fuzzy context.

  8. α-Cut method based importance measure for criticality analysis in fuzzy probability – Based fault tree analysis

    International Nuclear Information System (INIS)

    Purba, Julwan Hendry; Sony Tjahyani, D.T.; Widodo, Surip; Tjahjono, Hendro

    2017-01-01

    Highlights: •FPFTA deals with epistemic uncertainty using fuzzy probability. •Criticality analysis is important for reliability improvement. •An α-cut method based importance measure is proposed for criticality analysis in FPFTA. •The α-cut method based importance measure utilises α-cut multiplication, α-cut subtraction, and area defuzzification technique. •Benchmarking confirm that the proposed method is feasible for criticality analysis in FPFTA. -- Abstract: Fuzzy probability – based fault tree analysis (FPFTA) has been recently developed and proposed to deal with the limitations of conventional fault tree analysis. In FPFTA, reliabilities of basic events, intermediate events and top event are characterized by fuzzy probabilities. Furthermore, the quantification of the FPFTA is based on fuzzy multiplication rule and fuzzy complementation rule to propagate uncertainties from basic event to the top event. Since the objective of the fault tree analysis is to improve the reliability of the system being evaluated, it is necessary to find the weakest path in the system. For this purpose, criticality analysis can be implemented. Various importance measures, which are based on conventional probabilities, have been developed and proposed for criticality analysis in fault tree analysis. However, not one of those importance measures can be applied for criticality analysis in FPFTA, which is based on fuzzy probability. To be fully applied in nuclear power plant probabilistic safety assessment, FPFTA needs to have its corresponding importance measure. The objective of this study is to develop an α-cut method based importance measure to evaluate and rank the importance of basic events for criticality analysis in FPFTA. To demonstrate the applicability of the proposed measure, a case study is performed and its results are then benchmarked to the results generated by the four well known importance measures in conventional fault tree analysis. The results

  9. Automated Sleep Stage Scoring by Decision Tree Learning

    National Research Council Canada - National Science Library

    Hanaoka, Masaaki

    2001-01-01

    In this paper we describe a waveform recognition method that extracts characteristic parameters from wave- forms and a method of automated sleep stage scoring using decision tree learning that is in...

  10. Comparison of Greedy Algorithms for Decision Tree Optimization

    KAUST Repository

    Alkhalid, Abdulaziz

    2013-01-01

    This chapter is devoted to the study of 16 types of greedy algorithms for decision tree construction. The dynamic programming approach is used for construction of optimal decision trees. Optimization is performed relative to minimal values of average depth, depth, number of nodes, number of terminal nodes, and number of nonterminal nodes of decision trees. We compare average depth, depth, number of nodes, number of terminal nodes and number of nonterminal nodes of constructed trees with minimum values of the considered parameters obtained based on a dynamic programming approach. We report experiments performed on data sets from UCI ML Repository and randomly generated binary decision tables. As a result, for depth, average depth, and number of nodes we propose a number of good heuristics. © Springer-Verlag Berlin Heidelberg 2013.

  11. Decision tree approach for classification of remotely sensed satellite ...

    Indian Academy of Sciences (India)

    sensed satellite data using open source support. Richa Sharma .... Decision tree classification techniques have been .... the USGS Earth Resource Observation Systems. (EROS) ... for shallow water, 11% were for sparse and dense built-up ...

  12. Decision tree approach for classification of remotely sensed satellite

    Indian Academy of Sciences (India)

    DTC) algorithm for classification of remotely sensed satellite data (Landsat TM) using open source support. The decision tree is constructed by recursively partitioning the spectral distribution of the training dataset using WEKA, open source ...

  13. Transferability of decision trees for land cover classification in a ...

    African Journals Online (AJOL)

    This paper attempts to derive classification rules from training data of four Landsat-8 scenes by using the classification and regression tree (CART) implementation of the decision tree algorithm. The transferability of the ruleset was evaluated by classifying two adjacent scenes. The classification of the four mosaicked scenes ...

  14. Hesitant triangular fuzzy information aggregation operators based on Bonferroni means and their application to multiple attribute decision making.

    Science.gov (United States)

    Wang, Chunyong; Li, Qingguo; Zhou, Xiaoqiang; Yang, Tian

    2014-01-01

    We investigate the multiple attribute decision-making (MADM) problems with hesitant triangular fuzzy information. Firstly, definition and some operational laws of hesitant triangular fuzzy elements are introduced. Then, we develop some hesitant triangular fuzzy aggregation operators based on Bonferroni means and discuss their basic properties. Some existing operators can be viewed as their special cases. Next, we apply the proposed operators to deal with multiple attribute decision-making problems under hesitant triangular fuzzy environment. Finally, an illustrative example is given to show the developed method and demonstrate its practicality and effectiveness.

  15. Grey Language Hesitant Fuzzy Group Decision Making Method Based on Kernel and Grey Scale.

    Science.gov (United States)

    Li, Qingsheng; Diao, Yuzhu; Gong, Zaiwu; Hu, Aqin

    2018-03-02

    Based on grey language multi-attribute group decision making, a kernel and grey scale scoring function is put forward according to the definition of grey language and the meaning of the kernel and grey scale. The function introduces grey scale into the decision-making method to avoid information distortion. This method is applied to the grey language hesitant fuzzy group decision making, and the grey correlation degree is used to sort the schemes. The effectiveness and practicability of the decision-making method are further verified by the industry chain sustainable development ability evaluation example of a circular economy. Moreover, its simplicity and feasibility are verified by comparing it with the traditional grey language decision-making method and the grey language hesitant fuzzy weighted arithmetic averaging (GLHWAA) operator integration method after determining the index weight based on the grey correlation.

  16. Improved hybridization of Fuzzy Analytic Hierarchy Process (FAHP) algorithm with Fuzzy Multiple Attribute Decision Making - Simple Additive Weighting (FMADM-SAW)

    Science.gov (United States)

    Zaiwani, B. E.; Zarlis, M.; Efendi, S.

    2018-03-01

    In this research, the improvement of hybridization algorithm of Fuzzy Analytic Hierarchy Process (FAHP) with Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) in selecting the best bank chief inspector based on several qualitative and quantitative criteria with various priorities. To improve the performance of the above research, FAHP algorithm hybridization with Fuzzy Multiple Attribute Decision Making - Simple Additive Weighting (FMADM-SAW) algorithm was adopted, which applied FAHP algorithm to the weighting process and SAW for the ranking process to determine the promotion of employee at a government institution. The result of improvement of the average value of Efficiency Rate (ER) is 85.24%, which means that this research has succeeded in improving the previous research that is equal to 77.82%. Keywords: Ranking and Selection, Fuzzy AHP, Fuzzy TOPSIS, FMADM-SAW.

  17. TOPSIS-based consensus model for group decision-making with incomplete interval fuzzy preference relations.

    Science.gov (United States)

    Liu, Fang; Zhang, Wei-Guo

    2014-08-01

    Due to the vagueness of real-world environments and the subjective nature of human judgments, it is natural for experts to estimate their judgements by using incomplete interval fuzzy preference relations. In this paper, based on the technique for order preference by similarity to ideal solution method, we present a consensus model for group decision-making (GDM) with incomplete interval fuzzy preference relations. To do this, we first define a new consistency measure for incomplete interval fuzzy preference relations. Second, a goal programming model is proposed to estimate the missing interval preference values and it is guided by the consistency property. Third, an ideal interval fuzzy preference relation is constructed by using the induced ordered weighted averaging operator, where the associated weights of characterizing the operator are based on the defined consistency measure. Fourth, a similarity degree between complete interval fuzzy preference relations and the ideal one is defined. The similarity degree is related to the associated weights, and used to aggregate the experts' preference relations in such a way that more importance is given to ones with the higher similarity degree. Finally, a new algorithm is given to solve the GDM problem with incomplete interval fuzzy preference relations, which is further applied to partnership selection in formation of virtual enterprises.

  18. Multiple Attribute Group Decision-Making Methods Based on Trapezoidal Fuzzy Two-Dimensional Linguistic Partitioned Bonferroni Mean Aggregation Operators.

    Science.gov (United States)

    Yin, Kedong; Yang, Benshuo; Li, Xuemei

    2018-01-24

    In this paper, we investigate multiple attribute group decision making (MAGDM) problems where decision makers represent their evaluation of alternatives by trapezoidal fuzzy two-dimensional uncertain linguistic variable. To begin with, we introduce the definition, properties, expectation, operational laws of trapezoidal fuzzy two-dimensional linguistic information. Then, to improve the accuracy of decision making in some case where there are a sort of interrelationship among the attributes, we analyze partition Bonferroni mean (PBM) operator in trapezoidal fuzzy two-dimensional variable environment and develop two operators: trapezoidal fuzzy two-dimensional linguistic partitioned Bonferroni mean (TF2DLPBM) aggregation operator and trapezoidal fuzzy two-dimensional linguistic weighted partitioned Bonferroni mean (TF2DLWPBM) aggregation operator. Furthermore, we develop a novel method to solve MAGDM problems based on TF2DLWPBM aggregation operator. Finally, a practical example is presented to illustrate the effectiveness of this method and analyses the impact of different parameters on the results of decision-making.

  19. Multi-Attribute Decision-Making Based on Bonferroni Mean Operators under Cubic Intuitionistic Fuzzy Set Environment

    Directory of Open Access Journals (Sweden)

    Gagandeep Kaur

    2018-01-01

    Full Text Available Cubic intuitionistic fuzzy (CIF set is the hybrid set which can contain much more information to express an interval-valued intuitionistic fuzzy set and an intuitionistic fuzzy set simultaneously for handling the uncertainties in the data. Unfortunately, there has been no research on the aggregation operators on CIF sets so far. Since an aggregation operator is an important mathematical tool in decision-making problems, the present paper proposes some new Bonferroni mean and weighted Bonferroni mean averaging operators between the cubic intuitionistic fuzzy numbers for aggregating the different preferences of the decision-maker. Then, we develop a decision-making method based on the proposed operators under the cubic intuitionistic fuzzy environment and illustrated with a numerical example. Finally, a comparison analysis between the proposed and the existing approaches have been performed to illustrate the applicability and feasibility of the developed decision-making method.

  20. Sequence Algebra, Sequence Decision Diagrams and Dynamic Fault Trees

    International Nuclear Information System (INIS)

    Rauzy, Antoine B.

    2011-01-01

    A large attention has been focused on the Dynamic Fault Trees in the past few years. By adding new gates to static (regular) Fault Trees, Dynamic Fault Trees aim to take into account dependencies among events. Merle et al. proposed recently an algebraic framework to give a formal interpretation to these gates. In this article, we extend Merle et al.'s work by adopting a slightly different perspective. We introduce Sequence Algebras that can be seen as Algebras of Basic Events, representing failures of non-repairable components. We show how to interpret Dynamic Fault Trees within this framework. Finally, we propose a new data structure to encode sets of sequences of Basic Events: Sequence Decision Diagrams. Sequence Decision Diagrams are very much inspired from Minato's Zero-Suppressed Binary Decision Diagrams. We show that all operations of Sequence Algebras can be performed on this data structure.

  1. Make or buy decision considering uncertainty based on fuzzy logic using simulation and multiple criteria decision making

    Directory of Open Access Journals (Sweden)

    Ali Mohtashami

    2013-01-01

    Full Text Available Decision making on making/buying problem has always been a challenge to decision makers. In this paper a methodology has been proposed to resolve this challenge. This methodology is capable of evaluating making/buying decision making under uncertainty. For uncertainty, the fuzzy logic and simulation approaches have been used. The proposed methodology can be applied to parts with multi stage manufacturing processes and different suppliers. Therefore this methodology provides a scale for decision making from full outsourcing to full manufacturing and with selecting appropriate supplier.

  2. The Decision Tree for Teaching Management of Uncertainty

    Science.gov (United States)

    Knaggs, Sara J.; And Others

    1974-01-01

    A 'decision tree' consists of an outline of the patient's symptoms and a logic for decision and action. It is felt that this approach to the decisionmaking process better facilitates each learner's application of his own level of knowledge and skills. (Author)

  3. Ethnographic Decision Tree Modeling: A Research Method for Counseling Psychology.

    Science.gov (United States)

    Beck, Kirk A.

    2005-01-01

    This article describes ethnographic decision tree modeling (EDTM; C. H. Gladwin, 1989) as a mixed method design appropriate for counseling psychology research. EDTM is introduced and located within a postpositivist research paradigm. Decision theory that informs EDTM is reviewed, and the 2 phases of EDTM are highlighted. The 1st phase, model…

  4. Proactive data mining with decision trees

    CERN Document Server

    Dahan, Haim; Rokach, Lior; Maimon, Oded

    2014-01-01

    This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to another. It introduces a novel splitting crite

  5. Bounds on Average Time Complexity of Decision Trees

    KAUST Repository

    Chikalov, Igor

    2011-01-01

    In this chapter, bounds on the average depth and the average weighted depth of decision trees are considered. Similar problems are studied in search theory [1], coding theory [77], design and analysis of algorithms (e.g., sorting) [38]. For any diagnostic problem, the minimum average depth of decision tree is bounded from below by the entropy of probability distribution (with a multiplier 1/log2 k for a problem over a k-valued information system). Among diagnostic problems, the problems with a complete set of attributes have the lowest minimum average depth of decision trees (e.g, the problem of building optimal prefix code [1] and a blood test study in assumption that exactly one patient is ill [23]). For such problems, the minimum average depth of decision tree exceeds the lower bound by at most one. The minimum average depth reaches the maximum on the problems in which each attribute is "indispensable" [44] (e.g., a diagnostic problem with n attributes and kn pairwise different rows in the decision table and the problem of implementing the modulo 2 summation function). These problems have the minimum average depth of decision tree equal to the number of attributes in the problem description. © Springer-Verlag Berlin Heidelberg 2011.

  6. Robust stochastic fuzzy possibilistic programming for environmental decision making under uncertainty

    International Nuclear Information System (INIS)

    Zhang, Xiaodong; Huang, Guo H.; Nie, Xianghui

    2009-01-01

    Nonpoint source (NPS) water pollution is one of serious environmental issues, especially within an agricultural system. This study aims to propose a robust chance-constrained fuzzy possibilistic programming (RCFPP) model for water quality management within an agricultural system, where solutions for farming area, manure/fertilizer application amount, and livestock husbandry size under different scenarios are obtained and interpreted. Through improving upon the existing fuzzy possibilistic programming, fuzzy robust programming and chance-constrained programming approaches, the RCFPP can effectively reflect the complex system features under uncertainty, where implications of water quality/quantity restrictions for achieving regional economic development objectives are studied. By delimiting the uncertain decision space through dimensional enlargement of the original fuzzy constraints, the RCFPP enhances the robustness of the optimization processes and resulting solutions. The results of the case study indicate that useful information can be obtained through the proposed RCFPP model for providing feasible decision schemes for different agricultural activities under different scenarios (combinations of different p-necessity and p i levels). A p-necessity level represents the certainty or necessity degree of the imprecise objective function, while a p i level means the probabilities at which the constraints will be violated. A desire to acquire high agricultural income would decrease the certainty degree of the event that maximization of the objective be satisfied, and potentially violate water management standards; willingness to accept low agricultural income will run into the risk of potential system failure. The decision variables under combined p-necessity and p i levels were useful for the decision makers to justify and/or adjust the decision schemes for the agricultural activities through incorporation of their implicit knowledge. The results also suggest that

  7. PROCESSING THE INFORMATION CONTENT ON THE BASIS OF FUZZY NEURAL MODEL OF DECISION MAKING

    Directory of Open Access Journals (Sweden)

    Nina V. Komleva

    2013-01-01

    Full Text Available The article is devoted to the issues of mathematical modeling of the decision-making process of information content processing based on the fuzzy neural network TSK. Integral rating assessment of the content, which is necessary for taking a decision about its further usage, is made depended on varying characteristics. Mechanism for building individual trajectory and forming individual competence is provided to make the intellectual content search.

  8. A Fuzzy Decision Making Approach for Supplier Selection in Healthcare Industry

    OpenAIRE

    Zeynep Sener; Mehtap Dursun

    2014-01-01

    Supplier evaluation and selection is one of the most important components of an effective supply chain management system. Due to the expanding competition in healthcare, selecting the right medical device suppliers offers great potential for increasing quality while decreasing costs. This paper proposes a fuzzy decision making approach for medical supplier selection. A real-world medical device supplier selection problem is presented to illustrate the application of the proposed decision meth...

  9. Extensions of dynamic programming as a new tool for decision tree optimization

    KAUST Repository

    Alkhalid, Abdulaziz; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2013-01-01

    The chapter is devoted to the consideration of two types of decision trees for a given decision table: α-decision trees (the parameter α controls the accuracy of tree) and decision trees (which allow arbitrary level of accuracy). We study

  10. The Interval-Valued Triangular Fuzzy Soft Set and Its Method of Dynamic Decision Making

    OpenAIRE

    Xiaoguo Chen; Hong Du; Yue Yang

    2014-01-01

    A concept of interval-valued triangular fuzzy soft set is presented, and some operations of “AND,” “OR,” intersection, union and complement, and so forth are defined. Then some relative properties are discussed and several conclusions are drawn. A dynamic decision making model is built based on the definition of interval-valued triangular fuzzy soft set, in which period weight is determined by the exponential decay method. The arithmetic weighted average operator of interval-valued triangular...

  11. Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling

    Directory of Open Access Journals (Sweden)

    Nebot

    2012-04-01

    Full Text Available In this research a genetic fuzzy system (GFS is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR methodology and the Linguistic Rule FIR (LR-FIR algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR models and decision support (LR-FIR models. The GFS is evaluated in an e-learning context.

  12. An automatic iterative decision-making method for intuitionistic fuzzy linguistic preference relations

    Science.gov (United States)

    Pei, Lidan; Jin, Feifei; Ni, Zhiwei; Chen, Huayou; Tao, Zhifu

    2017-10-01

    As a new preference structure, the intuitionistic fuzzy linguistic preference relation (IFLPR) was recently introduced to efficiently deal with situations in which the membership and non-membership are represented as linguistic terms. In this paper, we study the issues of additive consistency and the derivation of the intuitionistic fuzzy weight vector of an IFLPR. First, the new concepts of order consistency, additive consistency and weak transitivity for IFLPRs are introduced, and followed by a discussion of the characterisation about additive consistent IFLPRs. Then, a parameterised transformation approach is investigated to convert the normalised intuitionistic fuzzy weight vector into additive consistent IFLPRs. After that, a linear optimisation model is established to derive the normalised intuitionistic fuzzy weights for IFLPRs, and a consistency index is defined to measure the deviation degree between an IFLPR and its additive consistent IFLPR. Furthermore, we develop an automatic iterative decision-making method to improve the IFLPRs with unacceptable additive consistency until the adjusted IFLPRs are acceptable additive consistent, and it helps the decision-maker to obtain the reasonable and reliable decision-making results. Finally, an illustrative example is provided to demonstrate the validity and applicability of the proposed method.

  13. Applying Fuzzy Decision Making Approach to IT Outsourcing Supplier Selection

    OpenAIRE

    Gülcin Büyüközkan; Mehmet Sakir Ersoy

    2009-01-01

    The decision of information technology (IT) outsourcing requires close attention to the evaluation of supplier selection process because the selection decision involves conflicting multiple criteria and is replete with complex decision making problems. Selecting the most appropriate suppliers is considered an important strategic decision that may impact the performance of outsourcing engagements. The objective of this paper is to aid decision makers to evaluate and assess possible IT outsourc...

  14. Alternative measures of risk of extreme events in decision trees

    International Nuclear Information System (INIS)

    Frohwein, H.I.; Lambert, J.H.; Haimes, Y.Y.

    1999-01-01

    A need for a methodology to control the extreme events, defined as low-probability, high-consequence incidents, in sequential decisions is identified. A variety of alternative and complementary measures of the risk of extreme events are examined for their usability as objective functions in sequential decisions, represented as single- or multiple-objective decision trees. Earlier work had addressed difficulties, related to non-separability, with the minimization of some measures of the risk of extreme events in sequential decisions. In an extension of these results, it is shown how some non-separable measures of the risk of extreme events can be interpreted in terms of separable constituents of risk, thereby enabling a wider class of measures of the risk of extreme events to be handled in a straightforward manner in a decision tree. Also for extreme events, results are given to enable minimax- and Hurwicz-criterion analyses in decision trees. An example demonstrates the incorporation of different measures of the risk of extreme events in a multi-objective decision tree. Conceptual formulations for optimizing non-separable measures of the risk of extreme events are identified as an important area for future investigation

  15. Multivariate analysis of flow cytometric data using decision trees.

    Science.gov (United States)

    Simon, Svenja; Guthke, Reinhard; Kamradt, Thomas; Frey, Oliver

    2012-01-01

    Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

  16. Hesitant fuzzy linguistic multicriteria decision-making method based on generalized prioritized aggregation operator.

    Science.gov (United States)

    Wu, Jia-ting; Wang, Jian-qiang; Wang, Jing; Zhang, Hong-yu; Chen, Xiao-hong

    2014-01-01

    Based on linguistic term sets and hesitant fuzzy sets, the concept of hesitant fuzzy linguistic sets was introduced. The focus of this paper is the multicriteria decision-making (MCDM) problems in which the criteria are in different priority levels and the criteria values take the form of hesitant fuzzy linguistic numbers (HFLNs). A new approach to solving these problems is proposed, which is based on the generalized prioritized aggregation operator of HFLNs. Firstly, the new operations and comparison method for HFLNs are provided and some linguistic scale functions are applied. Subsequently, two prioritized aggregation operators and a generalized prioritized aggregation operator of HFLNs are developed and applied to MCDM problems. Finally, an illustrative example is given to illustrate the effectiveness and feasibility of the proposed method, which are then compared to the existing approach.

  17. Intuitionistic Fuzzy Normalized Weighted Bonferroni Mean and Its Application in Multicriteria Decision Making

    Directory of Open Access Journals (Sweden)

    Wei Zhou

    2012-01-01

    Full Text Available The Bonferroni mean (BM was introduced by Bonferroni six decades ago but has been a hot research topic recently since its usefulness of the aggregation techniques. The desirable characteristic of the BM is its capability to capture the interrelationship between input arguments. However, the classical BM and GBM ignore the weight vector of aggregated arguments, the general weighted BM (WBM has not the reducibility, and the revised generalized weighted BM (GWBM cannot reflect the interrelationship between the individual criterion and other criteria. To deal with these issues, in this paper, we propose the normalized weighted Bonferroni mean (NWBM and the generalized normalized weighted Bonferroni mean (GNWBM and study their desirable properties, such as reducibility, idempotency, monotonicity, and boundedness. Furthermore, we investigate the NWBM and GNWBM operators under the intuitionistic fuzzy environment which is more common phenomenon in modern life and develop two new intuitionistic fuzzy aggregation operators based on the NWBM and GNWBM, that is, the intuitionistic fuzzy normalized weighted Bonferroni mean (IFNWBM and the generalized intuitionistic fuzzy normalized weighted Bonferroni mean (GIFNWBM. Finally, based on the GIFNWBM, we propose an approach to multicriteria decision making under the intuitionistic fuzzy environment, and a practical example is provided to illustrate our results.

  18. Decision Making in Uncertain Rural Scenarios by means of Fuzzy TOPSIS Method

    Directory of Open Access Journals (Sweden)

    Eva Armero

    2011-01-01

    Full Text Available A great deal of uncertain information which is difficult to quantify is taken into account by farmers and experts in the enterprise when making decisions. We are interested in the problems of the implementation of a rabbit-breeding farm. One of the first decisions to be taken refers to the design or type of structure for housing the animals, which is determined by the level of environmental control sought to be maintained in its interior. A farmer was consulted, and his answers were incorporated into the analysis, by means of the fuzzy TOPSIS methodology. The main purpose of this paper is to study the problem by means of the fuzzy TOPSIS method as multicriteria decision making, when the information was given in linguistic terms.

  19. Optimization of warehouse location through fuzzy multi-criteria decision making methods

    Directory of Open Access Journals (Sweden)

    C. L. Karmaker

    2015-07-01

    Full Text Available Strategic warehouse location-allocation problem is a multi-staged decision-making problem having both numerical and qualitative criteria. In order to survive in the global business scenario by improving supply chain performance, companies must examine the cross-functional drivers in the optimization of logistic systems. A meticulous observation makes evident that strategy warehouse location selection has become challenging as the number of alternatives and conflicting criteria increases. The issue becomes particularly problematic when the conventional concept has been applied in dealing with the imprecise nature of the linguistic assessment. The qualitative decisions for selection process are often complicated by the fact that often it is imprecise for the decision makers. Such problem must be overcome with defined efforts. Fuzzy multi-criteria decision making methods have been used in this research as aids in making location-allocation decisions. The anticipated methods in this research consist of two steps at its core. In the first step, the criteria of the existing problem are inspected and identified and then the weights of the sector and subsector are determined that have come to light by using Fuzzy AHP. In the second step, eligible alternatives are ranked by using TOPSIS and Fuzzy TOPSIS comparatively. A demonstration of the application of these methodologies in a real life problem is presented.

  20. Multiattribute Decision Making Based on Entropy under Interval-Valued Intuitionistic Fuzzy Environment

    Directory of Open Access Journals (Sweden)

    Yingjun Zhang

    2013-01-01

    Full Text Available Multiattribute decision making (MADM is one of the central problems in artificial intelligence, specifically in management fields. In most cases, this problem arises from uncertainty both in the data derived from the decision maker and the actions performed in the environment. Fuzzy set and high-order fuzzy sets were proven to be effective approaches in solving decision-making problems with uncertainty. Therefore, in this paper, we investigate the MADM problem with completely unknown attribute weights in the framework of interval-valued intuitionistic fuzzy (IVIF set (IVIFS. We first propose a new definition of IVIF entropy and some calculation methods for IVIF entropy. Furthermore, we propose an entropy-based decision-making method to solve IVIF MADM problems with completely unknown attribute weights. Particular emphasis is put on assessing the attribute weights based on IVIF entropy. Instead of the traditional methods, which use divergence among attributes or the probabilistic discrimination of attributes to obtain attribute weights, we utilize the IVIF entropy to assess the attribute weights based on the credibility of the decision-making matrix for solving the problem. Finally, a supplier selection example is given to demonstrate the feasibility and validity of the proposed MADM method.

  1. Minimizing size of decision trees for multi-label decision tables

    KAUST Repository

    Azad, Mohammad

    2014-09-29

    We used decision tree as a model to discover the knowledge from multi-label decision tables where each row has a set of decisions attached to it and our goal is to find out one arbitrary decision from the set of decisions attached to a row. The size of the decision tree can be small as well as very large. We study here different greedy as well as dynamic programming algorithms to minimize the size of the decision trees. When we compare the optimal result from dynamic programming algorithm, we found some greedy algorithms produce results which are close to the optimal result for the minimization of number of nodes (at most 18.92% difference), number of nonterminal nodes (at most 20.76% difference), and number of terminal nodes (at most 18.71% difference).

  2. Minimizing size of decision trees for multi-label decision tables

    KAUST Repository

    Azad, Mohammad; Moshkov, Mikhail

    2014-01-01

    We used decision tree as a model to discover the knowledge from multi-label decision tables where each row has a set of decisions attached to it and our goal is to find out one arbitrary decision from the set of decisions attached to a row. The size of the decision tree can be small as well as very large. We study here different greedy as well as dynamic programming algorithms to minimize the size of the decision trees. When we compare the optimal result from dynamic programming algorithm, we found some greedy algorithms produce results which are close to the optimal result for the minimization of number of nodes (at most 18.92% difference), number of nonterminal nodes (at most 20.76% difference), and number of terminal nodes (at most 18.71% difference).

  3. EEG feature selection method based on decision tree.

    Science.gov (United States)

    Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

    2015-01-01

    This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

  4. An Isometric Mapping Based Co-Location Decision Tree Algorithm

    Science.gov (United States)

    Zhou, G.; Wei, J.; Zhou, X.; Zhang, R.; Huang, W.; Sha, H.; Chen, J.

    2018-05-01

    Decision tree (DT) induction has been widely used in different pattern classification. However, most traditional DTs have the disadvantage that they consider only non-spatial attributes (ie, spectral information) as a result of classifying pixels, which can result in objects being misclassified. Therefore, some researchers have proposed a co-location decision tree (Cl-DT) method, which combines co-location and decision tree to solve the above the above-mentioned traditional decision tree problems. Cl-DT overcomes the shortcomings of the existing DT algorithms, which create a node for each value of a given attribute, which has a higher accuracy than the existing decision tree approach. However, for non-linearly distributed data instances, the euclidean distance between instances does not reflect the true positional relationship between them. In order to overcome these shortcomings, this paper proposes an isometric mapping method based on Cl-DT (called, (Isomap-based Cl-DT), which is a method that combines heterogeneous and Cl-DT together. Because isometric mapping methods use geodetic distances instead of Euclidean distances between non-linearly distributed instances, the true distance between instances can be reflected. The experimental results and several comparative analyzes show that: (1) The extraction method of exposed carbonate rocks is of high accuracy. (2) The proposed method has many advantages, because the total number of nodes, the number of leaf nodes and the number of nodes are greatly reduced compared to Cl-DT. Therefore, the Isomap -based Cl-DT algorithm can construct a more accurate and faster decision tree.

  5. AN ISOMETRIC MAPPING BASED CO-LOCATION DECISION TREE ALGORITHM

    Directory of Open Access Journals (Sweden)

    G. Zhou

    2018-05-01

    Full Text Available Decision tree (DT induction has been widely used in different pattern classification. However, most traditional DTs have the disadvantage that they consider only non-spatial attributes (ie, spectral information as a result of classifying pixels, which can result in objects being misclassified. Therefore, some researchers have proposed a co-location decision tree (Cl-DT method, which combines co-location and decision tree to solve the above the above-mentioned traditional decision tree problems. Cl-DT overcomes the shortcomings of the existing DT algorithms, which create a node for each value of a given attribute, which has a higher accuracy than the existing decision tree approach. However, for non-linearly distributed data instances, the euclidean distance between instances does not reflect the true positional relationship between them. In order to overcome these shortcomings, this paper proposes an isometric mapping method based on Cl-DT (called, (Isomap-based Cl-DT, which is a method that combines heterogeneous and Cl-DT together. Because isometric mapping methods use geodetic distances instead of Euclidean distances between non-linearly distributed instances, the true distance between instances can be reflected. The experimental results and several comparative analyzes show that: (1 The extraction method of exposed carbonate rocks is of high accuracy. (2 The proposed method has many advantages, because the total number of nodes, the number of leaf nodes and the number of nodes are greatly reduced compared to Cl-DT. Therefore, the Isomap -based Cl-DT algorithm can construct a more accurate and faster decision tree.

  6. Application of fuzzy decision making to countermeasure strategies after a nuclear accident

    International Nuclear Information System (INIS)

    Liu, X.; Ruan, D.

    1996-01-01

    In the event of a nuclear accident, any decision on countermeasures to protect the public should be made based upon the basic principles recommended by the International Commission on Radiological Protection. The application of these principles requires that there is a balance between the cost and the averted radiation dose, taking into account many subjective factors such as social/political acceptability, psychological stress, and the confidence of the population in the authorities etc. In the framework of classical methods, it is difficult to quantify human subjective judgements and the uncertainties of data efficiently. Hence, any attempt to find the optimal solution for countermeasure strategies without deliberative sensitivity analysis can be misleading. However, fuzzy sets, with linguistic terms to describe the human subjective judgement and with fuzzy numbers to model the uncertainties of the parameters, can be introduced to eliminate these difficulties. With fuzzy rating, a fuzzy multiple attribute decision making method can rank the possible countermeasure strategies. This paper will describe the procedure of the method and present an illustrative example

  7. Development of a fuzzy optimization model, supporting global warming decision-making

    International Nuclear Information System (INIS)

    Leimbach, M.

    1996-01-01

    An increasing number of models have been developed to support global warming response policies. The model constructors are facing a lot of uncertainties which limit the evidence of these models. The support of climate policy decision-making is only possible in a semi-quantitative way, as presented by a Fuzzy model. The model design is based on an optimization approach, integrated in a bounded risk decision-making framework. Given some regional emission-related and impact-related restrictions, optimal emission paths can be calculated. The focus is not only on carbon dioxide but on other greenhouse gases too. In the paper, the components of the model will be described. Cost coefficients, emission boundaries and impact boundaries are represented as Fuzzy parameters. The Fuzzy model will be transformed into a computational one by using an approach of Rommelfanger. In the second part, some problems of applying the model to computations will be discussed. This includes discussions on the data situation and the presentation, as well as interpretation of results of sensitivity analyses. The advantage of the Fuzzy approach is that the requirements regarding data precision are not so strong. Hence, the effort for data acquisition can be reduced and computations can be started earlier. 9 figs., 3 tabs., 17 refs., 1 appendix

  8. Fuzzy decision analysis for project scope change management

    Directory of Open Access Journals (Sweden)

    Farshad Shirazi

    2017-09-01

    Full Text Available It is very important to manage and control projects with the consideration of the triple constraints; namely time, cost and scope. It is also extremely important to manage the scope and all the procurements needed to complete any project. During the project’s lifecycle many changes take place, either positively or negatively, which should be controlled. If the changes are not controlled we may have scope creep that has negative effect on the project. It is commonly considered a negative incident, and thus, should be kept away from the project. By considering this concept, in this paper, we discuss scope change and managing scope and fuzzy analytical hierarchy process is used in selecting the best strategy to manage scope change in projects.

  9. USING PRECEDENTS FOR REDUCTION OF DECISION TREE BY GRAPH SEARCH

    Directory of Open Access Journals (Sweden)

    I. A. Bessmertny

    2015-01-01

    Full Text Available The paper considers the problem of mutual payment organization between business entities by means of clearing that is solved by search of graph paths. To reduce the decision tree complexity a method of precedents is proposed that consists in saving the intermediate solution during the moving along decision tree. An algorithm and example are presented demonstrating solution complexity coming close to a linear one. The tests carried out in civil aviation settlement system demonstrate approximately 30 percent shortage of real money transfer. The proposed algorithm is planned to be implemented also in other clearing organizations of the Russian Federation.

  10. Decision tree ensembles for online operation of large smart grids

    International Nuclear Information System (INIS)

    Steer, Kent C.B.; Wirth, Andrew; Halgamuge, Saman K.

    2012-01-01

    Highlights: ► We present a new technique for the online control of large smart grids. ► We use a Decision Tree Ensemble in a Receding Horizon Controller. ► Decision Trees can approximate online optimisation approaches. ► Decision Trees can make adjustments to their output in real time. ► The new technique outperforms heuristic online optimisation approaches. - Abstract: Smart grids utilise omnidirectional data transfer to operate a network of energy resources. Associated technologies present operators with greater control over system elements and more detailed information on the system state. While these features may improve the theoretical optimal operating performance, determining the optimal operating strategy becomes more difficult. In this paper, we show how a decision tree ensemble or ‘forest’ can produce a near-optimal control strategy in real time. The approach substitutes the decision forest for the simulation–optimisation sub-routine commonly employed in receding horizon controllers. The method is demonstrated on a small and a large network, and compared to controllers employing particle swarm optimisation and evolutionary strategies. For the smaller network the proposed method performs comparably in terms of total energy usage, but delivers a greater demand deficit. On the larger network the proposed method is superior with respect to all measures. We conclude that the method is useful when the time required to evaluate possible strategies via simulation is high.

  11. Hesitant Probabilistic Fuzzy Linguistic Sets with Applications in Multi-Criteria Group Decision Making Problems

    Directory of Open Access Journals (Sweden)

    Dheeraj Kumar Joshi

    2018-03-01

    Full Text Available Uncertainties due to randomness and fuzziness comprehensively exist in control and decision support systems. In the present study, we introduce notion of occurring probability of possible values into hesitant fuzzy linguistic element (HFLE and define hesitant probabilistic fuzzy linguistic set (HPFLS for ill structured and complex decision making problem. HPFLS provides a single framework where both stochastic and non-stochastic uncertainties can be efficiently handled along with hesitation. We have also proposed expected mean, variance, score and accuracy function and basic operations for HPFLS. Weighted and ordered weighted aggregation operators for HPFLS are also defined in the present study for its applications in multi-criteria group decision making (MCGDM problems. We propose a MCGDM method with HPFL information which is illustrated by an example. A real case study is also taken in the present study to rank State Bank of India, InfoTech Enterprises, I.T.C., H.D.F.C. Bank, Tata Steel, Tata Motors and Bajaj Finance using real data. Proposed HPFLS-based MCGDM method is also compared with two HFL-based decision making methods.

  12. Fuzzy Group Decision Making Approach for Ranking Work Stations Based on Physical Pressure

    Directory of Open Access Journals (Sweden)

    Hamed Salmanzadeh

    2014-06-01

    Full Text Available This paper proposes a Fuzzy Group Decision Making approach for ranking work stations based on physical pressure. Fuzzy group decision making approach allows experts to evaluate different ergonomic factors using linguistic terms such as very high, high, medium, low, very low, rather than precise numerical values. In this way, there is no need to measure parameters and evaluation can be easily made in a group. According to ergonomics much work contents and situations, accompanied with multiple parameters and uncertainties, fuzzy group decision making is the best way to evaluate such a chameleon of concept. A case study was down to utilize the approach and illustrate its application in ergonomic assessment and ranking the work stations based on work pressure and found that this approach provides flexibility, practicality, efficiency in making decision around ergonomics areas. The normalized defuzzification numbers which are resulted from this method are compared with result of quantitative assessment of Automotive Assembly Work Sheet auto, it’s demonstrated that the proposed method result is 10% less than Automotive Assembly Work Sheet, approximately.

  13. An Overview of Judgment and Decision Making Research Through the Lens of Fuzzy Trace Theory.

    Science.gov (United States)

    Setton, Roni; Wilhelms, Evan; Weldon, Becky; Chick, Christina; Reyna, Valerie

    2014-12-01

    We present the basic tenets of fuzzy trace theory, a comprehensive theory of memory, judgment, and decision making that is grounded in research on how information is stored as knowledge, mentally represented, retrieved from storage, and processed. In doing so, we highlight how it is distinguished from traditional models of decision making in that gist reasoning plays a central role. The theory also distinguishes advanced intuition from primitive impulsivity. It predicts that different sorts of errors occur with respect to each component of judgment and decision making: background knowledge, representation, retrieval, and processing. Classic errors in the judgment and decision making literature, such as risky-choice framing and the conjunction fallacy, are accounted for by fuzzy trace theory and new results generated by the theory contradict traditional approaches. We also describe how developmental changes in brain and behavior offer crucial insight into adult cognitive processing. Research investigating brain and behavior in developing and special populations supports fuzzy trace theory's predictions about reliance on gist processing.

  14. Fuzzy Reasoning as a Base for Collision Avoidance Decision Support System

    Directory of Open Access Journals (Sweden)

    tanja brcko

    2013-12-01

    Full Text Available Despite the generally high qualifications of seafarers, many maritime accidents are caused by human error; such accidents include capsizing, collision, and fire, and often result in pollution. Enough concern has been generated that researchers around the world have developed the study of the human factor into an independent scientific discipline. A great deal of progress has been made, particularly in the area of artificial intelligence. But since total autonomy is not yet expedient, the decision support systems based on soft computing are proposed to support human navigators and VTS operators in times of crisis as well as during the execution of everyday tasks as a means of reducing risk levels.This paper considers a decision support system based on fuzzy logic integrated into an existing bridge collision avoidance system. The main goal is to determine the appropriate course of avoidance, using fuzzy reasoning.

  15. Group Decision-Making for Hesitant Fuzzy Sets Based on Characteristic Objects Method

    Directory of Open Access Journals (Sweden)

    Shahzad Faizi

    2017-07-01

    Full Text Available There are many real-life problems that, because of the need to involve a wide domain of knowledge, are beyond a single expert. This is especially true for complex problems. Therefore, it is usually necessary to allocate more than one expert to a decision process. In such situations, we can observe an increasing importance of uncertainty. In this paper, the Multi-Criteria Decision-Making (MCDM method called the Characteristic Objects Method (COMET is extended to solve problems for Multi-Criteria Group Decision-Making (MCGDM in a hesitant fuzzy environment. It is a completely new idea for solving problems of group decision-making under uncertainty. In this approach, we use L-R-type Generalized Fuzzy Numbers (GFNs to get the degree of hesitancy for an alternative under a certain criterion. Therefore, the classical COMET method was adapted to work with GFNs in group decision-making problems. The proposed extension is presented in detail, along with the necessary background information. Finally, an illustrative numerical example is provided to elaborate the proposed method with respect to the support of a decision process. The presented extension of the COMET method, as opposed to others’ group decision-making methods, is completely free of the rank reversal phenomenon, which is identified as one of the most important MCDM challenges.

  16. Nature Disaster Risk Evaluation with a Group Decision Making Method Based on Incomplete Hesitant Fuzzy Linguistic Preference Relations

    Directory of Open Access Journals (Sweden)

    Ming Tang

    2018-04-01

    Full Text Available Because the natural disaster system is a very comprehensive and large system, the disaster reduction scheme must rely on risk analysis. Experts’ knowledge and experiences play a critical role in disaster risk assessment. The hesitant fuzzy linguistic preference relation is an effective tool to express experts’ preference information when comparing pairwise alternatives. Owing to the lack of knowledge or a heavy workload, information may be missed in the hesitant fuzzy linguistic preference relation. Thus, an incomplete hesitant fuzzy linguistic preference relation is constructed. In this paper, we firstly discuss some properties of the additive consistent hesitant fuzzy linguistic preference relation. Next, the incomplete hesitant fuzzy linguistic preference relation, the normalized hesitant fuzzy linguistic preference relation, and the acceptable hesitant fuzzy linguistic preference relation are defined. Afterwards, three procedures to estimate the missing information are proposed. The first one deals with the situation in which there are only n − 1 known judgments involving all the alternatives; the second one is used to estimate the missing information of the hesitant fuzzy linguistic preference relation with more known judgments; while the third procedure is used to deal with ignorance situations in which there is at least one alternative with totally missing information. Furthermore, an algorithm for group decision making with incomplete hesitant fuzzy linguistic preference relations is given. Finally, we illustrate our model with a case study about flood disaster risk evaluation. A comparative analysis is presented to testify the advantage of our method.

  17. Nature Disaster Risk Evaluation with a Group Decision Making Method Based on Incomplete Hesitant Fuzzy Linguistic Preference Relations.

    Science.gov (United States)

    Tang, Ming; Liao, Huchang; Li, Zongmin; Xu, Zeshui

    2018-04-13

    Because the natural disaster system is a very comprehensive and large system, the disaster reduction scheme must rely on risk analysis. Experts' knowledge and experiences play a critical role in disaster risk assessment. The hesitant fuzzy linguistic preference relation is an effective tool to express experts' preference information when comparing pairwise alternatives. Owing to the lack of knowledge or a heavy workload, information may be missed in the hesitant fuzzy linguistic preference relation. Thus, an incomplete hesitant fuzzy linguistic preference relation is constructed. In this paper, we firstly discuss some properties of the additive consistent hesitant fuzzy linguistic preference relation. Next, the incomplete hesitant fuzzy linguistic preference relation, the normalized hesitant fuzzy linguistic preference relation, and the acceptable hesitant fuzzy linguistic preference relation are defined. Afterwards, three procedures to estimate the missing information are proposed. The first one deals with the situation in which there are only n-1 known judgments involving all the alternatives; the second one is used to estimate the missing information of the hesitant fuzzy linguistic preference relation with more known judgments; while the third procedure is used to deal with ignorance situations in which there is at least one alternative with totally missing information. Furthermore, an algorithm for group decision making with incomplete hesitant fuzzy linguistic preference relations is given. Finally, we illustrate our model with a case study about flood disaster risk evaluation. A comparative analysis is presented to testify the advantage of our method.

  18. Hyper-parameter tuning of a decision tree induction algorithm

    NARCIS (Netherlands)

    Mantovani, R.G.; Horváth, T.; Cerri, R.; Vanschoren, J.; de Carvalho, A.C.P.L.F.

    2017-01-01

    Supervised classification is the most studied task in Machine Learning. Among the many algorithms used in such task, Decision Tree algorithms are a popular choice, since they are robust and efficient to construct. Moreover, they have the advantage of producing comprehensible models and satisfactory

  19. Practical secure decision tree learning in a teletreatment application

    NARCIS (Netherlands)

    de Hoogh, Sebastiaan; Schoenmakers, Berry; Chen, Ping; op den Akker, Harm

    In this paper we develop a range of practical cryptographic protocols for secure decision tree learning, a primary problem in privacy preserving data mining. We focus on particular variants of the well-known ID3 algorithm allowing a high level of security and performance at the same time. Our

  20. Practical secure decision tree learning in a teletreatment application

    NARCIS (Netherlands)

    Hoogh, de S.J.A.; Schoenmakers, B.; Chen, Ping; Op den Akker, H.; Christin, N.; Safavi-Naini, R.

    2014-01-01

    In this paper we develop a range of practical cryptographic protocols for secure decision tree learning, a primary problem in privacy preserving data mining. We focus on particular variants of the well-known ID3 algorithm allowing a high level of security and performance at the same time. Our

  1. Evaluation of Decision Trees for Cloud Detection from AVHRR Data

    Science.gov (United States)

    Shiffman, Smadar; Nemani, Ramakrishna

    2005-01-01

    Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

  2. New Splitting Criteria for Decision Trees in Stationary Data Streams.

    Science.gov (United States)

    Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Rutkowski, Leszek; Duda, Piotr; Jaworski, Maciej

    2018-06-01

    The most popular tools for stream data mining are based on decision trees. In previous 15 years, all designed methods, headed by the very fast decision tree algorithm, relayed on Hoeffding's inequality and hundreds of researchers followed this scheme. Recently, we have demonstrated that although the Hoeffding decision trees are an effective tool for dealing with stream data, they are a purely heuristic procedure; for example, classical decision trees such as ID3 or CART cannot be adopted to data stream mining using Hoeffding's inequality. Therefore, there is an urgent need to develop new algorithms, which are both mathematically justified and characterized by good performance. In this paper, we address this problem by developing a family of new splitting criteria for classification in stationary data streams and investigating their probabilistic properties. The new criteria, derived using appropriate statistical tools, are based on the misclassification error and the Gini index impurity measures. The general division of splitting criteria into two types is proposed. Attributes chosen based on type- splitting criteria guarantee, with high probability, the highest expected value of split measure. Type- criteria ensure that the chosen attribute is the same, with high probability, as it would be chosen based on the whole infinite data stream. Moreover, in this paper, two hybrid splitting criteria are proposed, which are the combinations of single criteria based on the misclassification error and Gini index.

  3. A Decision Tree for Nonmetric Sex Assessment from the Skull.

    Science.gov (United States)

    Langley, Natalie R; Dudzik, Beatrix; Cloutier, Alesia

    2018-01-01

    This study uses five well-documented cranial nonmetric traits (glabella, mastoid process, mental eminence, supraorbital margin, and nuchal crest) and one additional trait (zygomatic extension) to develop a validated decision tree for sex assessment. The decision tree was built and cross-validated on a sample of 293 U.S. White individuals from the William M. Bass Donated Skeletal Collection. Ordinal scores from the six traits were analyzed using the partition modeling option in JMP Pro 12. A holdout sample of 50 skulls was used to test the model. The most accurate decision tree includes three variables: glabella, zygomatic extension, and mastoid process. This decision tree yielded 93.5% accuracy on the training sample, 94% on the cross-validated sample, and 96% on a holdout validation sample. Linear weighted kappa statistics indicate acceptable agreement among observers for these variables. Mental eminence should be avoided, and definitions and figures should be referenced carefully to score nonmetric traits. © 2017 American Academy of Forensic Sciences.

  4. A framework for sensitivity analysis of decision trees.

    Science.gov (United States)

    Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

    2018-01-01

    In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

  5. Bounds on Average Time Complexity of Decision Trees

    KAUST Repository

    Chikalov, Igor

    2011-01-01

    In this chapter, bounds on the average depth and the average weighted depth of decision trees are considered. Similar problems are studied in search theory [1], coding theory [77], design and analysis of algorithms (e.g., sorting) [38]. For any

  6. Relationships between depth and number of misclassifications for decision trees

    KAUST Repository

    Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2011-01-01

    This paper describes a new tool for the study of relationships between depth and number of misclassifications for decision trees. In addition to the algorithm the paper also presents the results of experiments with three datasets from UCI Machine Learning Repository [3]. © 2011 Springer-Verlag.

  7. Shopping intention prediction using decision trees

    OpenAIRE

    Šebalj, Dario; Franjković, Jelena; Hodak, Kristina

    2017-01-01

    Introduction: The price is considered to be neglected marketing mix element due to the complexity of price management and sensitivity of customers on price changes. It pulls the fastest customer reactions to that change. Accordingly, the process of making shopping decisions can be very challenging for customer.Objective: The aim of this paper is to create a model that is able to predict shopping intention and classify respondents into one of the two categories, depending on whether they inten...

  8. An overview of decision tree applied to power systems

    DEFF Research Database (Denmark)

    Liu, Leo; Rather, Zakir Hussain; Chen, Zhe

    2013-01-01

    The corrosive volume of available data in electric power systems motivate the adoption of data mining techniques in the emerging field of power system data analytics. The mainstream of data mining algorithm applied to power system, Decision Tree (DT), also named as Classification And Regression...... Tree (CART), has gained increasing interests because of its high performance in terms of computational efficiency, uncertainty manageability, and interpretability. This paper presents an overview of a variety of DT applications to power systems for better interfacing of power systems with data...... analytics. The fundamental knowledge of CART algorithm is also introduced which is then followed by examples of both classification tree and regression tree with the help of case study for security assessment of Danish power system....

  9. Power Distribution System Planning Evaluation by a Fuzzy Multi-Criteria Group Decision Support System

    Directory of Open Access Journals (Sweden)

    Tiefeng Zhang

    2010-10-01

    Full Text Available The evaluation of solutions is an important phase in power distribution system planning (PDSP which allows issues such as quality of supply, cost, social service and environmental implications to be considered and usually involves the judgments of a group of experts. The planning problem is thus suitable for the multi-criteria group decision-making (MCGDM method. The evaluation process and evaluation criteria often involve uncertainties incorporated in quantitative analysis with crisp values and qualitative judgments with linguistic terms; therefore, fuzzy sets techniques are applied in this study. This paper proposes a fuzzy multi-criteria group decision-making (FMCGDM method for PDSP evaluation and applies a fuzzy multi-criteria group decision support system (FMCGDSS to support the evaluation task. We introduce a PDSP evaluation model, which has evaluation criteria within three levels, based on the characteristics of a power distribution system. A case-based example is performed on a test distribution network and demonstrates how all the problems in a PDSP evaluation are addressed using FMCGDSS. The results are acceptable to expert evaluators.

  10. Enhanced Decision Support Systems in Intensive Care Unit Based on Intuitionistic Fuzzy Sets

    Directory of Open Access Journals (Sweden)

    Hanen Jemal

    2017-01-01

    Full Text Available In areas of medical diagnosis and decision-making, several uncertainty and ambiguity shrouded situations are most often imposed. In this regard, one may well assume that intuitionistic fuzzy sets (IFS should stand as a potent technique useful for demystifying associated with the real healthcare decision-making situations. To this end, we are developing a prototype model helpful for detecting the patients risk degree in Intensive Care Unit (ICU. Based on the intuitionistic fuzzy sets, dubbed Medical Intuitionistic Fuzzy Expert Decision Support System (MIFEDSS, the shown work has its origins in the Modified Early Warning Score (MEWS standard. It is worth noting that the proposed prototype effectiveness validation is associated through a real case study test at the Polyclinic ESSALEMA cited in Sfax, Tunisia. This paper does actually provide some practical initial results concerning the system as carried out in real life situations. Indeed, the proposed system turns out to prove that the MIFEDSS does actually display an imposing capability for an established handily ICU related uncertainty issues. The performance of the prototypes is compared with the MEWS standard which exposed that the IFS application appears to perform highly better in deferring accuracy than the expert MEWS score with higher degrees of sensitivity and specificity being recorded.

  11. Intuitionistic fuzzy evidential power aggregation operator and its application in multiple criteria decision-making

    Science.gov (United States)

    Jiang, Wen; Wei, Boya

    2018-02-01

    The theory of intuitionistic fuzzy sets (IFS) is widely used for dealing with vagueness and the Dempster-Shafer (D-S) evidence theory has a widespread use in multiple criteria decision-making problems under uncertain situation. However, there are many methods to aggregate intuitionistic fuzzy numbers (IFNs), but the aggregation operator to fuse basic probability assignment (BPA) is rare. Power average (P-A) operator, as a powerful operator, is useful and important in information fusion. Motivated by the idea of P-A power, in this paper, a new operator based on the IFS and D-S evidence theory is proposed, which is named as intuitionistic fuzzy evidential power average (IFEPA) aggregation operator. First, an IFN is converted into a BPA, and the uncertainty is measured in D-S evidence theory. Second, the difference between BPAs is measured by Jousselme distance and a satisfying support function is proposed to get the support degree between each other effectively. Then the IFEPA operator is used for aggregating the original IFN and make a more reasonable decision. The proposed method is objective and reasonable because it is completely driven by data once some parameters are required. At the same time, it is novel and interesting. Finally, an application of developed models to the 'One Belt, One road' investment decision-making problems is presented to illustrate the effectiveness and feasibility of the proposed operator.

  12. Extensions of dynamic programming as a new tool for decision tree optimization

    KAUST Repository

    Alkhalid, Abdulaziz

    2013-01-01

    The chapter is devoted to the consideration of two types of decision trees for a given decision table: α-decision trees (the parameter α controls the accuracy of tree) and decision trees (which allow arbitrary level of accuracy). We study possibilities of sequential optimization of α-decision trees relative to different cost functions such as depth, average depth, and number of nodes. For decision trees, we analyze relationships between depth and number of misclassifications. We also discuss results of computer experiments with some datasets from UCI ML Repository. ©Springer-Verlag Berlin Heidelberg 2013.

  13. 'Misclassification error' greedy heuristic to construct decision trees for inconsistent decision tables

    KAUST Repository

    Azad, Mohammad; Moshkov, Mikhail

    2014-01-01

    A greedy algorithm has been presented in this paper to construct decision trees for three different approaches (many-valued decision, most common decision, and generalized decision) in order to handle the inconsistency of multiple decisions in a decision table. In this algorithm, a greedy heuristic ‘misclassification error’ is used which performs faster, and for some cost function, results are better than ‘number of boundary subtables’ heuristic in literature. Therefore, it can be used in the case of larger data sets and does not require huge amount of memory. Experimental results of depth, average depth and number of nodes of decision trees constructed by this algorithm are compared in the framework of each of the three approaches.

  14. An anonymization technique using intersected decision trees

    Directory of Open Access Journals (Sweden)

    Sam Fletcher

    2015-07-01

    Full Text Available Data mining plays an important role in analyzing the massive amount of data collected in today’s world. However, due to the public’s rising awareness of privacy and lack of trust in organizations, suitable Privacy Preserving Data Mining (PPDM techniques have become vital. A PPDM technique provides individual privacy while allowing useful data mining. We present a novel noise addition technique called Forest Framework, two novel data quality evaluation techniques called EDUDS and EDUSC, and a security evaluation technique called SERS. Forest Framework builds a decision forest from a dataset and preserves all the patterns (logic rules of the forest while adding noise to the dataset. We compare Forest Framework to its predecessor, Framework, and another established technique, GADP. Our comparison is done using our three evaluation criteria, as well as Prediction Accuracy. Our experimental results demonstrate the success of our proposed extensions to Framework and the usefulness of our evaluation criteria.

  15. Maclaurin symmetric mean operators of linguistic intuitionistic fuzzy numbers and their application to multiple-attribute decision-making

    Science.gov (United States)

    Liu, Peide; Qin, Xiyou

    2017-11-01

    Linguistic intuitionistic fuzzy number (LIFN) is a special intuitionistic fuzzy number which can more easily describe the vagueness existing in the real decision-making. Maclaurin symmetric mean (MSM) operator has the characteristic of considering the interrelationships among any number of input parameters. In this paper, we extended the MSM operator to the LIFNs and some extended MSM operators for LIFNs were proposed, some new decision-making methods were developed. Firstly, we introduced the definition, score function, properties and operational rules of the LIFNs. Then, we proposed some linguistic intuitionistic fuzzy MSM operators, such as linguistic intuitionistic fuzzy Maclaurin symmetric mean operator, weighted linguistic intuitionistic fuzzy Maclaurin symmetric mean (WLIFMSM) operator, linguistic intuitionistic fuzzy dual Maclaurin symmetric mean operator, weighted linguistic intuitionistic fuzzy dual Maclaurin symmetric mean (WLIFDMSM) operator. In the meantime, we studied some important properties of these operators, and developed some methods based on WLIFMSM operator and WLIFDMSM operator for multi-attribute decision-making. Finally, we use an example to demonstrate the effectiveness of the proposed methods.

  16. The Utility of Decision Trees in Oncofertility Care in Japan.

    Science.gov (United States)

    Ito, Yuki; Shiraishi, Eriko; Kato, Atsuko; Haino, Takayuki; Sugimoto, Kouhei; Okamoto, Aikou; Suzuki, Nao

    2017-03-01

    To identify the utility and issues associated with the use of decision trees in oncofertility patient care in Japan. A total of 35 women who had been diagnosed with cancer, but had not begun anticancer treatment, were enrolled. We applied the oncofertility decision tree for women published by Gardino et al. to counsel a consecutive series of women on fertility preservation (FP) options following cancer diagnosis. Percentage of women who decided to undergo oocyte retrieval for embryo cryopreservation and the expected live-birth rate for these patients were calculated using the following equation: expected live-birth rate = pregnancy rate at each age per embryo transfer × (1 - miscarriage rate) × No. of cryopreserved embryos. Oocyte retrieval was performed for 17 patients (48.6%; mean ± standard deviation [SD] age, 36.35 ± 3.82 years). The mean ± SD number of cryopreserved embryos was 5.29 ± 4.63. The expected live-birth rate was 0.66. The expected live-birth rate with FP indicated that one in three oncofertility patients would not expect to have a live birth following oocyte retrieval and embryo cryopreservation. While the decision trees were useful as decision-making tools for women contemplating FP, in the context of the current restrictions on oocyte donation and the extremely small number of adoptions in Japan, the remaining options for fertility after cancer are limited. In order for cancer survivors to feel secure in their decisions, the decision tree may need to be adapted simultaneously with improvements to the social environment, such as greater support for adoption.

  17. Three approaches to deal with inconsistent decision tables - Comparison of decision tree complexity

    KAUST Repository

    Azad, Mohammad; Chikalov, Igor; Moshkov, Mikhail

    2013-01-01

    In inconsistent decision tables, there are groups of rows with equal values of conditional attributes and different decisions (values of the decision attribute). We study three approaches to deal with such tables. Instead of a group of equal rows, we consider one row given by values of conditional attributes and we attach to this row: (i) the set of all decisions for rows from the group (many-valued decision approach); (ii) the most common decision for rows from the group (most common decision approach); and (iii) the unique code of the set of all decisions for rows from the group (generalized decision approach). We present experimental results and compare the depth, average depth and number of nodes of decision trees constructed by a greedy algorithm in the framework of each of the three approaches. © 2013 Springer-Verlag.

  18. Modeling and Testing Landslide Hazard Using Decision Tree

    Directory of Open Access Journals (Sweden)

    Mutasem Sh. Alkhasawneh

    2014-01-01

    Full Text Available This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID, Exhaustive CHAID, Classification and Regression Tree (CRT, and Quick-Unbiased-Efficient Statistical Tree (QUEST. Twenty-one factors were extracted using digital elevation models (DEMs and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0% compared to CHAID (81.9%, CRT (75.6%, and QUEST (74.0% model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.

  19. Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods Volume 2

    CERN Document Server

    Rao, R Venkata

    2013-01-01

    Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods presents the concepts and details of applications of MADM methods. A range of methods are covered including Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIšekriterijumsko KOmpromisno Rangiranje (VIKOR), Data Envelopment Analysis (DEA), Preference Ranking METHod for Enrichment Evaluations (PROMETHEE), ELimination Et Choix Traduisant la Realité (ELECTRE), COmplex PRoportional ASsessment (COPRAS), Grey Relational Analysis (GRA), UTility Additive (UTA), and Ordered Weighted Averaging (OWA). The existing MADM methods are improved upon and three novel multiple attribute decision making methods for solving the decision making problems of the manufacturing environment are proposed. The concept of integrated weights is introduced in the proposed subjective and objective integrated weights (SOIW) method and the weighted Euclidean distance ba...

  20. Computerized Adaptive Test vs. decision trees: Development of a support decision system to identify suicidal behavior.

    Science.gov (United States)

    Delgado-Gomez, D; Baca-Garcia, E; Aguado, D; Courtet, P; Lopez-Castroman, J

    2016-12-01

    Several Computerized Adaptive Tests (CATs) have been proposed to facilitate assessments in mental health. These tests are built in a standard way, disregarding useful and usually available information not included in the assessment scales that could increase the precision and utility of CATs, such as the history of suicide attempts. Using the items of a previously developed scale for suicidal risk, we compared the performance of a standard CAT and a decision tree in a support decision system to identify suicidal behavior. We included the history of past suicide attempts as a class for the separation of patients in the decision tree. The decision tree needed an average of four items to achieve a similar accuracy than a standard CAT with nine items. The accuracy of the decision tree, obtained after 25 cross-validations, was 81.4%. A shortened test adapted for the separation of suicidal and non-suicidal patients was developed. CATs can be very useful tools for the assessment of suicidal risk. However, standard CATs do not use all the information that is available. A decision tree can improve the precision of the assessment since they are constructed using a priori information. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. A fuzzy logic decision support system for assessing clinical nutritional risk

    Directory of Open Access Journals (Sweden)

    Ali Mohammad Hadianfard

    2015-04-01

    Full Text Available Introduction: Studies have indicated a global high prevalence of hospital malnutrition on admission and during hospitalization. Clinical Nutritional Risk Screen (CNRS is a way to identify malnutrition and manage nutritional interventions. Several traditional and non-computer based tools have been suggested for screening nutritional risk levels. The present study was an attempt to employ a computer based fuzzy model decision support system as a nutrition-screening tool for inpatients. Method: This is an applied modeling study. The system architecture was designed based on the fuzzy logic model including input data, inference engine, and output. A clinical nutritionist entered nineteen input variables using a windows-based graphical user interface. The inference engine was involved with knowledge obtained from literature and the construction of ‘IF-THEN’ rules. The output of the system was stratification of patients into four risk levels from ‘No’ to ‘High’ where a number was also allocated to them as a nutritional risk grade. All patients (121 people admitted during implementing the system participated in testing the model. The classification tests were used to measure the CNRS fuzzy model performance. IBM SPSS version 21 was utilized as a tool for data analysis with α = 0.05 as a significance level. Results: Results showed that sensitivity, specificity, accuracy, and precision of the fuzzy model performance were 91.67% (±4.92, 76% (±7.6, 88.43% (±5.7, and 93.62% (±4.32, respectively. Instant performance on admission and very low probability of mistake in predicting malnutrition risk level may justify using the model in hospitals. Conclusion: To conclude, the fuzzy model-screening tool is based on multiple nutritional risk factors, having the capability of classifying inpatients into several nutritional risk levels and identifying the level of required nutritional intervention.

  2. Improving the anesthetic process by a fuzzy rule based medical decision system.

    Science.gov (United States)

    Mendez, Juan Albino; Leon, Ana; Marrero, Ayoze; Gonzalez-Cava, Jose M; Reboso, Jose Antonio; Estevez, Jose Ignacio; Gomez-Gonzalez, José F

    2018-01-01

    The main objective of this research is the design and implementation of a new fuzzy logic tool for automatic drug delivery in patients undergoing general anesthesia. The aim is to adjust the drug dose to the real patient needs using heuristic knowledge provided by clinicians. A two-level computer decision system is proposed. The idea is to release the clinician from routine tasks so that he can focus on other variables of the patient. The controller uses the Bispectral Index (BIS) to assess the hypnotic state of the patient. Fuzzy controller was included in a closed-loop system to reach the BIS target and reject disturbances. BIS was measured using a BIS VISTA monitor, a device capable of calculating the hypnosis level of the patient through EEG information. An infusion pump with propofol 1% is used to supply the drug to the patient. The inputs to the fuzzy inference system are BIS error and BIS rate. The output is infusion rate increment. The mapping of the input information and the appropriate output is given by a rule-base based on knowledge of clinicians. To evaluate the performance of the fuzzy closed-loop system proposed, an observational study was carried out. Eighty one patients scheduled for ambulatory surgery were randomly distributed in 2 groups: one group using a fuzzy logic based closed-loop system (FCL) to automate the administration of propofol (42 cases); the second group using manual delivering of the drug (39 cases). In both groups, the BIS target was 50. The FCL, designed with intuitive logic rules based on the clinician experience, performed satisfactorily and outperformed the manual administration in patients in terms of accuracy through the maintenance stage. Copyright © 2018 Elsevier B.V. All rights reserved.

  3. A multicriteria decision making approach based on fuzzy theory and credibility mechanism for logistics center location selection.

    Science.gov (United States)

    Wang, Bowen; Xiong, Haitao; Jiang, Chengrui

    2014-01-01

    As a hot topic in supply chain management, fuzzy method has been widely used in logistics center location selection to improve the reliability and suitability of the logistics center location selection with respect to the impacts of both qualitative and quantitative factors. However, it does not consider the consistency and the historical assessments accuracy of experts in predecisions. So this paper proposes a multicriteria decision making model based on credibility of decision makers by introducing priority of consistency and historical assessments accuracy mechanism into fuzzy multicriteria decision making approach. In this way, only decision makers who pass the credibility check are qualified to perform the further assessment. Finally, a practical example is analyzed to illustrate how to use the model. The result shows that the fuzzy multicriteria decision making model based on credibility mechanism can improve the reliability and suitability of site selection for the logistics center.

  4. A fault tree analysis strategy using binary decision diagrams

    International Nuclear Information System (INIS)

    Reay, Karen A.; Andrews, John D.

    2002-01-01

    The use of binary decision diagrams (BDDs) in fault tree analysis provides both an accurate and efficient means of analysing a system. There is a problem, however, with the conversion process of the fault tree to the BDD. The variable ordering scheme chosen for the construction of the BDD has a crucial effect on its resulting size and previous research has failed to identify any scheme that is capable of producing BDDs for all fault trees. This paper proposes an analysis strategy aimed at increasing the likelihood of obtaining a BDD for any given fault tree, by ensuring the associated calculations are as efficient as possible. The method implements simplification techniques, which are applied to the fault tree to obtain a set of 'minimal' subtrees, equivalent to the original fault tree structure. BDDs are constructed for each, using ordering schemes most suited to their particular characteristics. Quantitative analysis is performed simultaneously on the set of BDDs to obtain the top event probability, the system unconditional failure intensity and the criticality of the basic events

  5. A fuzzy multi-criteria decision-making model for trigeneration system

    International Nuclear Information System (INIS)

    Wang Jiangjiang; Jing Youyin; Zhang Chunfa; Shi Guohua; Zhang Xutao

    2008-01-01

    The decision making for trigeneration systems is a compositive project and it should be evaluated and compared in a multi-criteria analysis method. This paper presents a fuzzy multi-criteria decision-making model (FMCDM) for trigeneration systems selection and evaluation. The multi-criteria decision-making methods are briefly reviewed combining the general decision-making process. Then the fuzzy set theory, weighting method and the FMCDM model are presented. Finally, several kinds of trigeneration systems, whose dynamical sources are, respectively stirling engine, gas turbine, gas engine and solid oxide fuel cell, are compared and evaluated with a separate generation system. The case for selecting the optimal trigeneration system applied to a residential building is assessed from the technical, economical, environmental and social aspects, and the FMCDM model combining analytic hierarchical process is applied to the trigeneration case to demonstrate the decision-making process and effectiveness of proposed model. The results show that the gas engine plus lithium bromide absorption water heater/chiller unit for the residential building is the best scheme in the five options

  6. Fuzzy multi-objective decision making on a low and intermediate level waste repository safety assessment

    International Nuclear Information System (INIS)

    Lemos, Francisco Luiz de; Deshpande, Ashok; Guimaraes, Lamartine

    2002-01-01

    Low and intermediate waste disposal facilities safety assessment is comprised of several steps from site selection , construction and operation to post-closure performance assessment. This is a multidisciplinary and complex task , and can not be analyzed by one expert only. This high complexity can lead to ambiguity and vagueness in information and consequently in the decision making process. In order to make the decision process clear and objective, there is the need to provide the decision makers with a clear and comprehensive picture of the whole process and, at the same time, simple and easily understandable by the public. This paper suggests the development of an inference system based on fuzzy decision making methodology. Fuzzy logic tools are specially suited to deal with ambiguous data by using language expressions. This process would be capable of integrating knowledge from various fields of environmental sciences. It has an advantage of keeping record of reasoning for each intermediate decision that lead to the final results which makes it more dependable and defensible as well. (author)

  7. Assessing experience in the deliberate practice of running using a fuzzy decision-support system

    Science.gov (United States)

    Roveri, Maria Isabel; Manoel, Edison de Jesus; Onodera, Andrea Naomi; Ortega, Neli R. S.; Tessutti, Vitor Daniel; Vilela, Emerson; Evêncio, Nelson

    2017-01-01

    The judgement of skill experience and its levels is ambiguous though it is crucial for decision-making in sport sciences studies. We developed a fuzzy decision support system to classify experience of non-elite distance runners. Two Mamdani subsystems were developed based on expert running coaches’ knowledge. In the first subsystem, the linguistic variables of training frequency and volume were combined and the output defined the quality of running practice. The second subsystem yielded the level of running experience from the combination of the first subsystem output with the number of competitions and practice time. The model results were highly consistent with the judgment of three expert running coaches (r>0.88, p0.86, p<0.001). From the expert’s knowledge and the fuzzy model, running experience is beyond the so-called "10-year rule" and depends not only on practice time, but on the quality of practice (training volume and frequency) and participation in competitions. The fuzzy rule-based model was very reliable, valid, deals with the marked ambiguities inherent in the judgment of experience and has potential applications in research, sports training, and clinical settings. PMID:28817655

  8. Assessing experience in the deliberate practice of running using a fuzzy decision-support system.

    Directory of Open Access Journals (Sweden)

    Maria Isabel Roveri

    Full Text Available The judgement of skill experience and its levels is ambiguous though it is crucial for decision-making in sport sciences studies. We developed a fuzzy decision support system to classify experience of non-elite distance runners. Two Mamdani subsystems were developed based on expert running coaches' knowledge. In the first subsystem, the linguistic variables of training frequency and volume were combined and the output defined the quality of running practice. The second subsystem yielded the level of running experience from the combination of the first subsystem output with the number of competitions and practice time. The model results were highly consistent with the judgment of three expert running coaches (r>0.88, p0.86, p<0.001. From the expert's knowledge and the fuzzy model, running experience is beyond the so-called "10-year rule" and depends not only on practice time, but on the quality of practice (training volume and frequency and participation in competitions. The fuzzy rule-based model was very reliable, valid, deals with the marked ambiguities inherent in the judgment of experience and has potential applications in research, sports training, and clinical settings.

  9. Decision framework of photovoltaic module selection under interval-valued intuitionistic fuzzy environment

    International Nuclear Information System (INIS)

    Long, Shengping; Geng, Shuai

    2015-01-01

    Highlights: • The evaluation index system is set by the engineering and supply chain perspectives. • The interval-valued intuitionistic fuzzy set (IVIFS) to express the performances. • The IVIFS entropy weight method is applied to improve the objectivity of weights. - Abstract: The selection of appropriate photovoltaic module is of extremely high importance for the solar power station project; however the comprehensive problem of evaluation index system, the information loss problem and the lack-objectivity problem in the selection process will decrease the reasonability of the selection result. The innovation points of this paper are as follows: first, the comprehensive evaluation index system of photovoltaic module is established from the engineering management and supply chain management perspectives to solve the comprehensive problem; second, the interval-valued intuitionistic fuzzy set (IVIFS) are introduced into the photovoltaic modules selection process to express the alternatives’ performances to solve the information loss problem; third, the IVIFS entropy weight method is applied to improve the objectivity of the criteria’s weights. According to the aforementioned solutions, the decision framework of photovoltaic module selection under interval-valued intuitionistic fuzzy environment are established and used in a case study to demonstrate its effectiveness. Therefore, from the theoretical modeling and empirical demonstration, the decision framework proposed in this paper can effectively handle such a complicated problem and lead to an outstanding result.

  10. Total Path Length and Number of Terminal Nodes for Decision Trees

    KAUST Repository

    Hussain, Shahid

    2014-01-01

    This paper presents a new tool for study of relationships between total path length (average depth) and number of terminal nodes for decision trees. These relationships are important from the point of view of optimization of decision trees

  11. Project Delivery System Mode Decision Based on Uncertain AHP and Fuzzy Sets

    Science.gov (United States)

    Kaishan, Liu; Huimin, Li

    2017-12-01

    The project delivery system mode determines the contract pricing type, project management mode and the risk allocation among all participants. Different project delivery system modes have different characteristics and applicable scope. For the owners, the selection of the delivery mode is the key point to decide whether the project can achieve the expected benefits, it relates to the success or failure of project construction. Under the precondition of comprehensively considering the influence factors of the delivery mode, the model of project delivery system mode decision was set up on the basis of uncertain AHP and fuzzy sets, which can well consider the uncertainty and fuzziness when conducting the index evaluation and weight confirmation, so as to rapidly and effectively identify the most suitable delivery mode according to project characteristics. The effectiveness of the model has been verified via the actual case analysis in order to provide reference for the construction project delivery system mode.

  12. Model Multi Criteria Decision Making with Fuzzy ANP Method for Performance Measurement Small Medium Enterprise (SME)

    Science.gov (United States)

    Rahmanita, E.; Widyaningrum, V. T.; Kustiyahningsih, Y.; Purnama, J.

    2018-04-01

    SMEs have a very important role in the development of the economy in Indonesia. SMEs assist the government in terms of creating new jobs and can support household income. The number of SMEs in Madura and the number of measurement indicators in the SME mapping so that it requires a method.This research uses Fuzzy Analytic Network Process (FANP) method for performance measurement SME. The FANP method can handle data that contains uncertainty. There is consistency index in determining decisions. Performance measurement in this study is based on a perspective of the Balanced Scorecard. This research approach integrated internal business perspective, learning, and growth perspective and fuzzy Analytic Network Process (FANP). The results of this research areframework a priority weighting of assessment indicators SME.

  13. Integrating GIS with fuzzy multi-criteria decision making for suitable wind farm locations

    Energy Technology Data Exchange (ETDEWEB)

    Iyappan, L.; Pandian, P.K. [Tagore Engineering College. Dept. of Civil Engineering, Tamil Nadu (India)

    2012-07-01

    Wind Energy is spatial in nature and the degree of potential wind farm locations are fuzzy i.e., the boundaries among highly, moderate and least suitable is not clear cut. The study area of this research covers entire taluk of Tirumangalam, Madurai District (India). In this study, to help wind energy companies, policy-makers and investors in evaluating potential wind farm locations in the Tirumangalam Taluk (Tamil Nadu, India), an adaptation of a Geographical Information System (GIS) and Fuzzy Multi-criteria Decision Making(FMDM) approach is attended. The entire processes were completed by using open source GIS software (Quantum GIS and GRASS GIS) with help of freely available data. The software tool takes inputs such as wind power density, Slope, Transmission lines, environmental factors, and economic factors to provide an in-depth analysis for suitable location options. (Author)

  14. Online decision trees to support the control of gastrointestinal worms in ruminants

    OpenAIRE

    Koopmann, Regine; Dämmrich, Michaela; Ploeger, Harm

    2014-01-01

    Control of gastrointestinal worms is crucial to any pasture system for ruminants. To support the farmer's foresighted planning of pasturage and to avoid excessive deworm-ing in Germany we created four decision trees and put them online. They are freely accessible at www.weide-parasiten.de. There is one decision tree for young first season cattle in intensive dairy husbandry, one decision tree for young cattle in suckling-cow management and one decision tree for sheep and goats, respectively.

  15. Multi-Attribute Decision-Making Based on Prioritized Aggregation Operator under Hesitant Intuitionistic Fuzzy Linguistic Environment

    Directory of Open Access Journals (Sweden)

    Peide Liu

    2017-11-01

    Full Text Available A hesitant intuitionistic fuzzy linguistic set (HIFLS that integrates both qualitative and quantitative evaluations is an extension of the linguistic set, intuitionistic fuzzy set (IFS, hesitant fuzzy set (HFS and hesitant intuitionistic fuzzy set (HIFS. It can describe the qualitative evaluation information given by the decision-makers (DMs and reflect their uncertainty. In this article, we defined some new operational laws and comparative method for HIFLSs. Then, based on these operations, we propose two prioritized aggregation (PA operators for HIFLSs: prioritized weighted averaging operator for HIFLSs (HIFLPWA and prioritized weighted geometric operator for HIFLSs (HIFLPWG. Based on these aggregation operators, an approach for multi-attribute decision-making (MADM is developed under the environment of HIFLSs. Finally, a practical example is given to show the practicality and effectiveness of the developed approach by comparing with the other representative methods.

  16. APPROACHES TO LENIENCY REDUCTION IN MULTI-CRITERIA DECISION MAKING WITH INTERVAL-VALUED FUZZY SETS AND AN EXPERIMENTAL ANALYSIS

    OpenAIRE

    TING-YU CHEN

    2012-01-01

    The purpose of this paper is to present a useful method for estimating the importance of criteria and reducing the leniency bias in multiple criteria decision analysis based on interval-valued fuzzy sets. Several types of net predispositions are defined to represent an aggregated effect of interval-valued fuzzy evaluations. The suitability function for measuring the overall evaluation of each alternative is then determined based on simple additive weighting (SAW) methods. Because positive or ...

  17. NONLINEAR ASSIGNMENT-BASED METHODS FOR INTERVAL-VALUED INTUITIONISTIC FUZZY MULTI-CRITERIA DECISION ANALYSIS WITH INCOMPLETE PREFERENCE INFORMATION

    OpenAIRE

    TING-YU CHEN

    2012-01-01

    In the context of interval-valued intuitionistic fuzzy sets, this paper develops nonlinear assignment-based methods to manage imprecise and uncertain subjective ratings under incomplete preference structures and thereby determines the optimal ranking order of the alternatives for multiple criteria decision analysis. By comparing each interval-valued intuitionistic fuzzy number's score function, accuracy function, membership uncertainty index, and hesitation uncertainty index, a ranking proced...

  18. Dual Processes in Decision Making and Developmental Neuroscience: A Fuzzy-Trace Model.

    Science.gov (United States)

    Reyna, Valerie F; Brainerd, Charles J

    2011-09-01

    From Piaget to the present, traditional and dual-process theories have predicted improvement in reasoning from childhood to adulthood, and improvement has been observed. However, developmental reversals-that reasoning biases emerge with development -have also been observed in a growing list of paradigms. We explain how fuzzy-trace theory predicts both improvement and developmental reversals in reasoning and decision making. Drawing on research on logical and quantitative reasoning, as well as on risky decision making in the laboratory and in life, we illustrate how the same small set of theoretical principles apply to typical neurodevelopment, encompassing childhood, adolescence, and adulthood, and to neurological conditions such as autism and Alzheimer's disease. For example, framing effects-that risk preferences shift when the same decisions are phrases in terms of gains versus losses-emerge in early adolescence as gist-based intuition develops. In autistic individuals, who rely less on gist-based intuition and more on verbatim-based analysis, framing biases are attenuated (i.e., they outperform typically developing control subjects). In adults, simple manipulations based on fuzzy-trace theory can make framing effects appear and disappear depending on whether gist-based intuition or verbatim-based analysis is induced. These theoretical principles are summarized and integrated in a new mathematical model that specifies how dual modes of reasoning combine to produce predictable variability in performance. In particular, we show how the most popular and extensively studied model of decision making-prospect theory-can be derived from fuzzy-trace theory by combining analytical (verbatim-based) and intuitive (gist-based) processes.

  19. Dual Processes in Decision Making and Developmental Neuroscience: A Fuzzy-Trace Model

    Science.gov (United States)

    Reyna, Valerie F.; Brainerd, Charles J.

    2011-01-01

    From Piaget to the present, traditional and dual-process theories have predicted improvement in reasoning from childhood to adulthood, and improvement has been observed. However, developmental reversals—that reasoning biases emerge with development —have also been observed in a growing list of paradigms. We explain how fuzzy-trace theory predicts both improvement and developmental reversals in reasoning and decision making. Drawing on research on logical and quantitative reasoning, as well as on risky decision making in the laboratory and in life, we illustrate how the same small set of theoretical principles apply to typical neurodevelopment, encompassing childhood, adolescence, and adulthood, and to neurological conditions such as autism and Alzheimer's disease. For example, framing effects—that risk preferences shift when the same decisions are phrases in terms of gains versus losses—emerge in early adolescence as gist-based intuition develops. In autistic individuals, who rely less on gist-based intuition and more on verbatim-based analysis, framing biases are attenuated (i.e., they outperform typically developing control subjects). In adults, simple manipulations based on fuzzy-trace theory can make framing effects appear and disappear depending on whether gist-based intuition or verbatim-based analysis is induced. These theoretical principles are summarized and integrated in a new mathematical model that specifies how dual modes of reasoning combine to produce predictable variability in performance. In particular, we show how the most popular and extensively studied model of decision making—prospect theory—can be derived from fuzzy-trace theory by combining analytical (verbatim-based) and intuitive (gist-based) processes. PMID:22096268

  20. Nicotine replacement therapy decision based on fuzzy multi-criteria analysis

    Science.gov (United States)

    Tarmudi, Zamali; Matmali, Norfazillah; Abdullah, Mohd Lazim

    2017-08-01

    It has been observed that Nicotine Replacement Therapy (NRT) is one of the alternatives to control and reduce smoking addiction among smokers. Since the decision to choose the best NRT alternative involves uncertainty, ambiguity factors and diverse input datasets, thus, this paper proposes a fuzzy multi-criteria analysis (FMA) to overcome these issues. It focuses on how the fuzzy approach can unify the diversity of datasets based on NRT's decision-making problem. The analysis done employed the advantage of the cost-benefit criterion to unify the mixture of dataset input. The performance matrix was utilised to derive the performance scores. An empirical example regarding the NRT's decision-making problem was employed to illustrate the proposed approach. Based on the calculations, this analytical approach was found to be highly beneficial in terms of usability. It was also very applicable and efficient in dealing with the mixture of input datasets. Hence, the decision-making process can easily be used by experts and patients who are interested to join the therapy/cessation program.

  1. Fuzzy Based Decision Support System for Condition Assessment and Rating of Bridges

    Science.gov (United States)

    Srinivas, Voggu; Sasmal, Saptarshi; Karusala, Ramanjaneyulu

    2016-09-01

    In this work, a knowledge based decision support system has been developed to efficiently handle the issues such as distress diagnosis, assessment of damages and condition rating of existing bridges towards developing an exclusive and robust Bridge Management System (BMS) for sustainable bridges. The Knowledge Based Expert System (KBES) diagnoses the distresses and finds the cause of distress in the bridge by processing the data which are heuristic and combined with site inspection results, laboratory test results etc. The coupling of symbolic and numeric type of data has been successfully implemented in the expert system to strengthen its decision making process. Finally, the condition rating of the bridge is carried out using the assessment results obtained from the KBES and the information received from the bridge inspector. A systematic procedure has been developed using fuzzy mathematics for condition rating of bridges by combining the fuzzy weighted average and resolution identity technique. The proposed methodologies and the decision support system will facilitate in developing a robust and exclusive BMS for a network of bridges across the country and allow the bridge engineers and decision makers to carry out maintenance of bridges in a rational and systematic way.

  2. Constructing an optimal decision tree for FAST corner point detection

    KAUST Repository

    Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail

    2011-01-01

    In this paper, we consider a problem that is originated in computer vision: determining an optimal testing strategy for the corner point detection problem that is a part of FAST algorithm [11,12]. The problem can be formulated as building a decision tree with the minimum average depth for a decision table with all discrete attributes. We experimentally compare performance of an exact algorithm based on dynamic programming and several greedy algorithms that differ in the attribute selection criterion. © 2011 Springer-Verlag.

  3. Generalized concavity in fuzzy optimization and decision analysis

    CERN Document Server

    Ramík, Jaroslav

    2002-01-01

    Convexity of sets in linear spaces, and concavity and convexity of functions, lie at the root of beautiful theoretical results that are at the same time extremely useful in the analysis and solution of optimization problems, including problems of either single objective or multiple objectives. Not all of these results rely necessarily on convexity and concavity; some of the results can guarantee that each local optimum is also a global optimum, giving these methods broader application to a wider class of problems. Hence, the focus of the first part of the book is concerned with several types of generalized convex sets and generalized concave functions. In addition to their applicability to nonconvex optimization, these convex sets and generalized concave functions are used in the book's second part, where decision-making and optimization problems under uncertainty are investigated. Uncertainty in the problem data often cannot be avoided when dealing with practical problems. Errors occur in real-world data for...

  4. Fuzzy Multi-actor Multi-criteria Decision Making for Sustainability Assessment of biomass-based technologies for hydrogen production

    DEFF Research Database (Denmark)

    Ren, Jingzheng; Fedele, Andrea; Mason, Marco

    2013-01-01

    The purpose of this paper is to develop a sustainability assessment method to rank the prior sequence of biomass-based technologies for hydrogen production. A novel fuzzy Multi-actor Multi-criteria Decision Making method which allows multiple groups of decision-makers to use linguistic variables...

  5. Embedded system in Arduino platform with Fuzzy control to support the grain aeration decision

    Directory of Open Access Journals (Sweden)

    Albino Szesz Junior

    Full Text Available ABSTRACT: Aeration is currently the most commonly used technique to improve the drying and storage of grain, depending on temperature and water content of the grain, as of the temperature and relative humidity of the outside air. In order to monitor temperature and humidity of the grain mass, it is possible to have a network of sensors in the cells of both internal and external storage. Use of artificial intelligence through Fuzzy theory, has been used since the 60s and enables their application on various forms. Thus, it is observed that the aeration of grain in function of representing a system of controlled environment can be studied in relation to the application of this theory. Therefore, the aim of this paper is to present an embedded Fuzzy control system based on the mathematical model of CRUZ et al. (2002 and applied to the Arduino platform, for decision support in aeration of grain. For this, an embedded Arduino system was developed, which received the environmental values of temperature and humidity to then be processed in a Fuzzy controller and return the output as a recommendation to control the aeration process rationally. Comparing the results obtained from the graph presented by LASSERAN (1981 it was observed that the system is effective.

  6. An Intuitionistic Fuzzy Stochastic Decision-Making Method Based on Case-Based Reasoning and Prospect Theory

    Directory of Open Access Journals (Sweden)

    Peng Li

    2017-01-01

    Full Text Available According to the case-based reasoning method and prospect theory, this paper mainly focuses on finding a way to obtain decision-makers’ preferences and the criterion weights for stochastic multicriteria decision-making problems and classify alternatives. Firstly, we construct a new score function for an intuitionistic fuzzy number (IFN considering the decision-making environment. Then, we aggregate the decision-making information in different natural states according to the prospect theory and test decision-making matrices. A mathematical programming model based on a case-based reasoning method is presented to obtain the criterion weights. Moreover, in the original decision-making problem, we integrate all the intuitionistic fuzzy decision-making matrices into an expectation matrix using the expected utility theory and classify or rank the alternatives by the case-based reasoning method. Finally, two illustrative examples are provided to illustrate the implementation process and applicability of the developed method.

  7. Classification and Optimization of Decision Trees for Inconsistent Decision Tables Represented as MVD Tables

    KAUST Repository

    Azad, Mohammad

    2015-10-11

    Decision tree is a widely used technique to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples (objects) with equal values of conditional attributes but different decisions (values of the decision attribute), then to discover the essential patterns or knowledge from the data set is challenging. We consider three approaches (generalized, most common and many-valued decision) to handle such inconsistency. We created different greedy algorithms using various types of impurity and uncertainty measures to construct decision trees. We compared the three approaches based on the decision tree properties of the depth, average depth and number of nodes. Based on the result of the comparison, we choose to work with the many-valued decision approach. Now to determine which greedy algorithms are efficient, we compared them based on the optimization and classification results. It was found that some greedy algorithms Mult\\\\_ws\\\\_entSort, and Mult\\\\_ws\\\\_entML are good for both optimization and classification.

  8. Classification and Optimization of Decision Trees for Inconsistent Decision Tables Represented as MVD Tables

    KAUST Repository

    Azad, Mohammad; Moshkov, Mikhail

    2015-01-01

    Decision tree is a widely used technique to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples (objects) with equal values of conditional attributes but different decisions (values of the decision attribute), then to discover the essential patterns or knowledge from the data set is challenging. We consider three approaches (generalized, most common and many-valued decision) to handle such inconsistency. We created different greedy algorithms using various types of impurity and uncertainty measures to construct decision trees. We compared the three approaches based on the decision tree properties of the depth, average depth and number of nodes. Based on the result of the comparison, we choose to work with the many-valued decision approach. Now to determine which greedy algorithms are efficient, we compared them based on the optimization and classification results. It was found that some greedy algorithms Mult\\_ws\\_entSort, and Mult\\_ws\\_entML are good for both optimization and classification.

  9. The use of Fuzzy expert system in robots decision-making

    International Nuclear Information System (INIS)

    Jamaseb, Mehdi; Jafari, Shahram; Montaseri, Farshid; Dadgar, Masoud

    2014-01-01

    The main issue that is investigated in this paper, is a method for decision making of mobile robots in different conditions for this purpose, we have used expert system. In this way, that the conditions of the robot are analyzed by on expert person a special issue (like following a ball) using knowledge base and suitable decisions will be mode. Then, using this information fuzzy rules well be built, and using its rules, robots decisions can be implemented like an expert person. In this study, we have used delta3d base for implementing expert systems and CLIPS and also we have used NAO for simulation rcssserver3d robot and 3d football simulation have been used for implementing operation program

  10. Implementation of Fuzzy Decision to Control Patient Room Facilities using Eye Blink

    Science.gov (United States)

    Zaeni, Ilham A. E.; Wibawa, Aji P.; Aripriharta; Sendari, Siti

    2018-04-01

    This study proposed the implementation of Fuzzy decision to control patient’s room facilities. In this study, four icons were sequentially displayed on the computer screen. The icons representing four option that can be selected by the patient is including switch the light on/off, switch the fan on/off, moving the bed’s backrest downward, and moving the bed’s backrest upward. The eye blink was extracted from subject’s electroencephalograph (EEG) signals which acquired from the FP1 region. The attention was also extracted from subject’s EEG signals to ensure that subject concentrate to the task. The eye blink and attention level were used for Fuzzy decision inputs, while the output is a decision that states the selection is valid or not. The selected option is the command that appears on the screen when the selection is valid. In this study, subjects were asked to choose each command several times and the accuracy was computed based on the number of correct selection.

  11. A consensus model for group decision making under interval type-2 fuzzy environment

    Institute of Scientific and Technical Information of China (English)

    Xiao-xiong ZHANG; Bing-feng GE; Yue-jin TAN

    2016-01-01

    We propose a new consensus model for group decision making (GDM) problems, using an interval type-2 fuzzy environment. In our model, experts are asked to express their preferences using linguistic terms characterized by interval type-2 fuzzy sets (IT2 FSs), because these can provide decision makers with greater freedom to express the vagueness in real-life situa-tions. Consensus and proximity measures based on the arithmetic operations of IT2 FSs are used simultaneously to guide the decision-making process. The majority of previous studies have taken into account only the importance of the experts in the aggregation process, which may give unreasonable results. Thus, we propose a new feedback mechanism that generates different advice strategies for experts according to their levels of importance. In general, experts with a lower level of importance require a larger number of suggestions to change their initial preferences. Finally, we investigate a numerical example and execute com-parable models and ours, to demonstrate the performance of our proposed model. The results indicate that the proposed model provides greater insight into the GDM process.

  12. Decision making using AHP (Analytic Hierarchy Process) and fuzzy set theory in waste management

    International Nuclear Information System (INIS)

    Chung, J.Y.; Lee, K.J.; Kim, C.D.

    1995-01-01

    The major problem is how to consider the differences in opinions, when many experts are involved in decision making process. This paper provides a simple general methodology to treat the differences in various opinions. The authors determined the grade of membership through the process of magnitude estimation derived from pairwise comparisons and AHP developed by Saaty. They used fuzzy set theory to consider the differences in opinions and obtain the priorities for each alternative. An example, which can be applied to radioactive waste management, also was presented. The result shows a good agreement with the results of averaging methods

  13. Method of decision tree applied in adopting the decision for promoting a company

    Directory of Open Access Journals (Sweden)

    Cezarina Adina TOFAN

    2015-09-01

    Full Text Available The decision can be defined as the way chosen from several possible to achieve an objective. An important role in the functioning of the decisional-informational system is held by the decision-making methods. Decision trees are proving to be very useful tools for taking financial decisions or regarding the numbers, where a large amount of complex information must be considered. They provide an effective structure in which alternative decisions and the implications of their choice can be assessed, and help to form a correct and balanced vision of the risks and rewards that may result from a certain choice. For these reasons, the content of this communication will review a series of decision-making criteria. Also, it will analyse the benefits of using the decision tree method in the decision-making process by providing a numerical example. On this basis, it can be concluded that the procedure may prove useful in making decisions for companies operating on markets where competition intensity is differentiated.

  14. The Best Path Analysis in Military Highway Transport Based on DEA and Multiobjective Fuzzy Decision-Making

    Directory of Open Access Journals (Sweden)

    Wu Juan

    2014-01-01

    Full Text Available Military transport path selection directly affects the transport speed, efficiency, and safety. To a certain degree, the results of the path selection determine success or failure of the war situation. The purpose of this paper is to propose a model based on DEA (data envelopment analysis and multiobjective fuzzy decision-making for path selection. The path decision set is established according to a search algorithm based on overlapping section punishment. Considering the influence of various fuzzy factors, the model of optimal path is constructed based on DEA and multitarget fuzzy decision-making theory, where travel time, transport risk, quick response capability, and transport cost constitute the evaluation target set. A reasonable path set can be calculated and sorted according to the comprehensive scores of the paths. The numerical results show that the model and the related algorithms are effective for path selection of military transport.

  15. Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network

    Directory of Open Access Journals (Sweden)

    Zhuqing Bi

    2017-01-01

    Full Text Available According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient.

  16. Fuzzy fault tree assessment based on improved AHP for fire and explosion accidents for steel oil storage tanks.

    Science.gov (United States)

    Shi, Lei; Shuai, Jian; Xu, Kui

    2014-08-15

    Fire and explosion accidents of steel oil storage tanks (FEASOST) occur occasionally during the petroleum and chemical industry production and storage processes and often have devastating impact on lives, the environment and property. To contribute towards the development of a quantitative approach for assessing the occurrence probability of FEASOST, a fault tree of FEASOST is constructed that identifies various potential causes. Traditional fault tree analysis (FTA) can achieve quantitative evaluation if the failure data of all of the basic events (BEs) are available, which is almost impossible due to the lack of detailed data, as well as other uncertainties. This paper makes an attempt to perform FTA of FEASOST by a hybrid application between an expert elicitation based improved analysis hierarchy process (AHP) and fuzzy set theory, and the occurrence possibility of FEASOST is estimated for an oil depot in China. A comparison between statistical data and calculated data using fuzzy fault tree analysis (FFTA) based on traditional and improved AHP is also made. Sensitivity and importance analysis has been performed to identify the most crucial BEs leading to FEASOST that will provide insights into how managers should focus effective mitigation. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Measuring performance in health care: case-mix adjustment by boosted decision trees.

    Science.gov (United States)

    Neumann, Anke; Holstein, Josiane; Le Gall, Jean-Roger; Lepage, Eric

    2004-10-01

    The purpose of this paper is to investigate the suitability of boosted decision trees for the case-mix adjustment involved in comparing the performance of various health care entities. First, we present logistic regression, decision trees, and boosted decision trees in a unified framework. Second, we study in detail their application for two common performance indicators, the mortality rate in intensive care and the rate of potentially avoidable hospital readmissions. For both examples the technique of boosting decision trees outperformed standard prognostic models, in particular linear logistic regression models, with regard to predictive power. On the other hand, boosting decision trees was computationally demanding and the resulting models were rather complex and needed additional tools for interpretation. Boosting decision trees represents a powerful tool for case-mix adjustment in health care performance measurement. Depending on the specific priorities set in each context, the gain in predictive power might compensate for the inconvenience in the use of boosted decision trees.

  18. Interval-Valued Hesitant Fuzzy Multiattribute Group Decision Making Based on Improved Hamacher Aggregation Operators and Continuous Entropy

    Directory of Open Access Journals (Sweden)

    Jun Liu

    2017-01-01

    Full Text Available Under the interval-valued hesitant fuzzy information environment, we investigate a multiattribute group decision making (MAGDM method with continuous entropy weights and improved Hamacher information aggregation operators. Firstly, we introduce the axiomatic definition of entropy for interval-valued hesitant fuzzy elements (IVHFEs and construct a continuous entropy formula on the basis of the continuous ordered weighted averaging (COWA operator. Then, based on the Hamacher t-norm and t-conorm, the adjusted operational laws for IVHFEs are defined. In order to aggregate interval-valued hesitant fuzzy information, some new improved interval-valued hesitant fuzzy Hamacher aggregation operators are investigated, including the improved interval-valued hesitant fuzzy Hamacher ordered weighted averaging (I-IVHFHOWA operator and the improved interval-valued hesitant fuzzy Hamacher ordered weighted geometric (I-IVHFHOWG operator, the desirable properties of which are discussed. In addition, the relationship among these proposed operators is analyzed in detail. Applying the continuous entropy and the proposed operators, an approach to MAGDM is developed. Finally, a numerical example for emergency operating center (EOC selection is provided, and comparative analyses with existing methods are performed to demonstrate that the proposed approach is both valid and practical to deal with group decision making problems.

  19. A new intuitionistic fuzzy rule-based decision-making system for an operating system process scheduler.

    Science.gov (United States)

    Butt, Muhammad Arif; Akram, Muhammad

    2016-01-01

    We present a new intuitionistic fuzzy rule-based decision-making system based on intuitionistic fuzzy sets for a process scheduler of a batch operating system. Our proposed intuitionistic fuzzy scheduling algorithm, inputs the nice value and burst time of all available processes in the ready queue, intuitionistically fuzzify the input values, triggers appropriate rules of our intuitionistic fuzzy inference engine and finally calculates the dynamic priority (dp) of all the processes in the ready queue. Once the dp of every process is calculated the ready queue is sorted in decreasing order of dp of every process. The process with maximum dp value is sent to the central processing unit for execution. Finally, we show complete working of our algorithm on two different data sets and give comparisons with some standard non-preemptive process schedulers.

  20. 5th International Conference on Fuzzy and Neuro Computing

    CERN Document Server

    Panigrahi, Bijaya; Das, Swagatam; Suganthan, Ponnuthurai

    2015-01-01

    This proceedings bring together contributions from researchers from academia and industry to report the latest cutting edge research made in the areas of Fuzzy Computing, Neuro Computing and hybrid Neuro-Fuzzy Computing in the paradigm of Soft Computing. The FANCCO 2015 conference explored new application areas, design novel hybrid algorithms for solving different real world application problems. After a rigorous review of the 68 submissions from all over the world, the referees panel selected 27 papers to be presented at the Conference. The accepted papers have a good, balanced mix of theory and applications. The techniques ranged from fuzzy neural networks, decision trees, spiking neural networks, self organizing feature map, support vector regression, adaptive neuro fuzzy inference system, extreme learning machine, fuzzy multi criteria decision making, machine learning, web usage mining, Takagi-Sugeno Inference system, extended Kalman filter, Goedel type logic, fuzzy formal concept analysis, biclustering e...

  1. Influence diagrams and decision trees for severe accident management

    Energy Technology Data Exchange (ETDEWEB)

    Goetz, W.W.J.

    1996-09-01

    A review of relevant methodologies based on Influence Diagrams (IDs), Decision Trees (DTs), and Containment Event Trees (CETs) was conducted to assess the practicality of these methods for the selection of effective strategies for Severe Accident Management (SAM). The review included an evaluation of some software packages for these methods. The emphasis was on possible pitfalls of using IDs and on practical aspects, the latter by performance of a case study that was based on an existing Level 2 Probabilistic Safety Assessment (PSA). The study showed that the use of a combined ID/DT model has advantages over CET models, in particular when conservatisms in the Level 2 PSA have been identified and replaced by fair assessments of the uncertainties involved. It is recommended to use ID/DT models complementary to CET models. (orig.).

  2. Influence diagrams and decision trees for severe accident management

    International Nuclear Information System (INIS)

    Goetz, W.W.J.; Seebregts, A.J.; Bedford, T.J.

    1996-08-01

    A review of relevent methodologies based on Influence Diagrams (IDs), Decision Trees (DTs), and Containment Event Trees (CETs) was conducted to assess the practicality of these methods for the selection of effective strategies for Severe Accident Management (SAM). The review included an evaluation of some software packages for these methods. The emphasis was on possible pitfalls of using IDs and on practical aspects, the latter by performance of a case study that was based on an existing Level 2 Probabilistic Safety Assessment (PSA). The study showed that the use of a combined ID/DT model has advantages over CET models, in particular when conservatisms in the Level 2 PSA have been identified and replaced by fair assessments of the uncertainties involved. It is recommended to use ID/DT models as complementary to CET models. (orig.)

  3. Influence diagrams and decision trees for severe accident management

    International Nuclear Information System (INIS)

    Goetz, W.W.J.

    1996-09-01

    A review of relevant methodologies based on Influence Diagrams (IDs), Decision Trees (DTs), and Containment Event Trees (CETs) was conducted to assess the practicality of these methods for the selection of effective strategies for Severe Accident Management (SAM). The review included an evaluation of some software packages for these methods. The emphasis was on possible pitfalls of using IDs and on practical aspects, the latter by performance of a case study that was based on an existing Level 2 Probabilistic Safety Assessment (PSA). The study showed that the use of a combined ID/DT model has advantages over CET models, in particular when conservatisms in the Level 2 PSA have been identified and replaced by fair assessments of the uncertainties involved. It is recommended to use ID/DT models complementary to CET models. (orig.)

  4. Decision model on the demographic profile for tuberculosis control using fuzzy logic

    Directory of Open Access Journals (Sweden)

    Laisa Ribeiro de Sá

    2015-06-01

    Full Text Available This study aimed to describe the relationship between demographic factors and the involvement of tuberculosis by applying a decision support model based on fuzzy logic to classify the regions as priority and non-priority in the city of João Pessoa, state of Paraíba (PB. As data source, we used the Notifiable Diseases Information System between 2009 and 2011. We chose the descriptive analysis, relative risk (RR, spatial distribution and fuzzy logic. The total of 1,245 cases remained in the study, accounting for 37.02% of cases in 2009. High and low risk clusters were identified, and the RR was higher among men (8.47, with 12 clusters, and among those uneducated (11.65, with 13 clusters. To demonstrate the functionality of the model was elected the year with highest number of cases, and the municipality district with highest population. The methodology identified priority areas, guiding managers to make decisions that respect the local particularities.

  5. A Scientific Decision Framework for Supplier Selection under Interval Valued Intuitionistic Fuzzy Environment

    Directory of Open Access Journals (Sweden)

    R. Krishankumar

    2017-01-01

    Full Text Available This paper proposes a new scientific decision framework (SDF under interval valued intuitionistic fuzzy (IVIF environment for supplier selection (SS. The framework consists of two phases, where, in the first phase, criteria weights are estimated in a sensible manner using newly proposed IVIF based statistical variance (SV method and, in the second phase, the suitable supplier is selected using ELECTRE (ELimination and Choice Expressing REality ranking method under IVIF environment. This method involves three categories of outranking, namely, strong, moderate, and weak. Previous studies on ELECTRE ranking reveal that scholars have only used two categories of outranking, namely, strong and weak, in the formulation of IVIF based ELECTRE, which eventually aggravates fuzziness and vagueness in decision making process due to the potential loss of information. Motivated by this challenge, third outranking category, called moderate, is proposed, which considerably reduces the loss of information by improving checks to the concordance and discordance matrices. Thus, in this paper, IVIF-ELECTRE (IVIFE method is presented and popular TOPSIS method is integrated with IVIFE for obtaining a linear ranking. Finally, the practicality of the proposed framework is demonstrated using SS example and the strength of proposed SDF is realized by comparing the framework with other similar methods.

  6. Totally Optimal Decision Trees for Monotone Boolean Functions with at Most Five Variables

    KAUST Repository

    Chikalov, Igor

    2013-01-01

    In this paper, we present the empirical results for relationships between time (depth) and space (number of nodes) complexity of decision trees computing monotone Boolean functions, with at most five variables. We use Dagger (a tool for optimization of decision trees and decision rules) to conduct experiments. We show that, for each monotone Boolean function with at most five variables, there exists a totally optimal decision tree which is optimal with respect to both depth and number of nodes.

  7. Fault trees for decision making in systems analysis

    International Nuclear Information System (INIS)

    Lambert, H.E.

    1975-01-01

    The application of fault tree analysis (FTA) to system safety and reliability is presented within the framework of system safety analysis. The concepts and techniques involved in manual and automated fault tree construction are described and their differences noted. The theory of mathematical reliability pertinent to FTA is presented with emphasis on engineering applications. An outline of the quantitative reliability techniques of the Reactor Safety Study is given. Concepts of probabilistic importance are presented within the fault tree framework and applied to the areas of system design, diagnosis and simulation. The computer code IMPORTANCE ranks basic events and cut sets according to a sensitivity analysis. A useful feature of the IMPORTANCE code is that it can accept relative failure data as input. The output of the IMPORTANCE code can assist an analyst in finding weaknesses in system design and operation, suggest the most optimal course of system upgrade, and determine the optimal location of sensors within a system. A general simulation model of system failure in terms of fault tree logic is described. The model is intended for efficient diagnosis of the causes of system failure in the event of a system breakdown. It can also be used to assist an operator in making decisions under a time constraint regarding the future course of operations. The model is well suited for computer implementation. New results incorporated in the simulation model include an algorithm to generate repair checklists on the basis of fault tree logic and a one-step-ahead optimization procedure that minimizes the expected time to diagnose system failure. (80 figures, 20 tables)

  8. Data acquisition in modeling using neural networks and decision trees

    Directory of Open Access Journals (Sweden)

    R. Sika

    2011-04-01

    Full Text Available The paper presents a comparison of selected models from area of artificial neural networks and decision trees in relation with actualconditions of foundry processes. The work contains short descriptions of used algorithms, their destination and method of data preparation,which is a domain of work of Data Mining systems. First part concerns data acquisition realized in selected iron foundry, indicating problems to solve in aspect of casting process modeling. Second part is a comparison of selected algorithms: a decision tree and artificial neural network, that is CART (Classification And Regression Trees and BP (Backpropagation in MLP (Multilayer Perceptron networks algorithms.Aim of the paper is to show an aspect of selecting data for modeling, cleaning it and reducing, for example due to too strong correlationbetween some of recorded process parameters. Also, it has been shown what results can be obtained using two different approaches:first when modeling using available commercial software, for example Statistica, second when modeling step by step using Excel spreadsheetbasing on the same algorithm, like BP-MLP. Discrepancy of results obtained from these two approaches originates from a priorimade assumptions. Mentioned earlier Statistica universal software package, when used without awareness of relations of technologicalparameters, i.e. without user having experience in foundry and without scheduling ranks of particular parameters basing on acquisition, can not give credible basis to predict the quality of the castings. Also, a decisive influence of data acquisition method has been clearly indicated, the acquisition should be conducted according to repetitive measurement and control procedures. This paper is based on about 250 records of actual data, for one assortment for 6 month period, where only 12 data sets were complete (including two that were used for validation of neural network and useful for creating a model. It is definitely too

  9. Risk perception and communication in vaccination decisions: a fuzzy-trace theory approach.

    Science.gov (United States)

    Reyna, Valerie F

    2012-05-28

    The tenets of fuzzy-trace theory, along with prior research on risk perception and risk communication, are used to develop a process model of vaccination decisions in the era of Web 2.0. The theory characterizes these decisions in terms of background knowledge, dual mental representations (verbatim and gist), retrieval of values, and application of values to representations in context. Lack of knowledge interferes with the ability to extract the essential meaning, or gist, of vaccination messages. Prevention decisions have, by definition, a status quo option of "feeling okay." Psychological evidence from other prevention decisions, such as cancer screening, indicates that many people initially mentally represent their decision options in terms of simple, categorical gist: a choice between (a) a feeling-okay option (e.g., the unvaccinated status quo) versus (b) taking up preventive behavior that can have two potential categorical outcomes: feeling okay or not feeling okay. Hence, applying the same theoretical rules as used to explain framing effects and the Allais paradox, the decision to get a flu shot, for example, boils down to feeling okay (not sick) versus feeling okay (not sick) or not feeling okay (sick, side effects, or death). Because feeling okay is superior to not feeling okay (a retrieved value), this impoverished gist supports choosing not to have the flu vaccine. Anti-vaccination sources provide more coherent accounts of the gist of vaccination than official sources, filling a need to understand rare adverse outcomes. Copyright © 2011 Elsevier Ltd. All rights reserved.

  10. When Irrational Biases Are Smart: A Fuzzy-Trace Theory of Complex Decision Making

    Directory of Open Access Journals (Sweden)

    Valerie Reyna

    2018-06-01

    Full Text Available I take a decision-making approach to consider ways of addressing the “unresolved and dramatic problems in the world”. Traditional approaches to good decision-making are reviewed. These approaches reduce complex decisions to tradeoffs between magnitudes of probabilities, and outcomes in which the quantity and precision of information are key to making good decisions. I discuss a contrasting framework, called “fuzzy-trace theory”, which emphasizes understanding the simple gist of options and applying core social and moral values. Importantly, the tendency to rely on meaningful but simple gist increases from childhood to adulthood (or, in adulthood, as people gain experience in a domain, so that specific irrational biases grow with knowledge and experience. As predicted theoretically, these violations of rationality in the traditional sense are associated empirically with healthier and more adaptive outcomes. Thus, interventions that help decision makers understand the essential gist of their options and how it connects to core values are practical approaches to reducing “unresolved and dramatic problems in the world” one decision at a time.

  11. Entropy lower bounds of quantum decision tree complexity

    OpenAIRE

    Shi, Yaoyun

    2000-01-01

    We prove a general lower bound of quantum decision tree complexity in terms of some entropy notion. We regard the computation as a communication process in which the oracle and the computer exchange several rounds of messages, each round consisting of O(log(n)) bits. Let E(f) be the Shannon entropy of the random variable f(X), where X is uniformly random in f's domain. Our main result is that it takes \\Omega(E(f)) queries to compute any \\emph{total} function f. It is interesting to contrast t...

  12. Language Adaptive LVCSR Through Polyphone Decision Tree Specialization

    Science.gov (United States)

    2000-08-01

    crossing rate. After cepstral mean subtraction a linear discriminant analysis 1Mandarin is given in character based error rate, Japanese in hiragana ...data. For this purpose we German GE 9173 71 132K 16.7 introduce a polyphone decision tree specialization method. Several Japanese JA 9096 108 212K...12.1 2. Multiple Languages German 11.8 61K 200 44.5 43 9.0 For our experiments we developed monolingual LVCSR sys- Japanese 10.0 22K 230 33.8 33 7.9

  13. A MOORA based fuzzy multi-criteria decision making approach for supply chain strategy selection

    Directory of Open Access Journals (Sweden)

    Bijan Sarkar

    2012-08-01

    Full Text Available To acquire the competitive advantages in order to survive in the global business scenario, modern companies are now facing the problems of selecting key supply chain strategies. Strategy selection becomes difficult as the number of alternatives and conflicting criteria increases. Multi criteria decision making (MCDM methodologies help the supply chain managers take a lead in a complex industrial set-up. The present investigation applies fuzzy MCDM technique entailing multi-objective optimization on the basis of ratio analysis (MOORA in selection of alternatives in a supply chain. The MOORA method is utilized to three suitable numerical examples for the selection of supply chain strategies (warehouse location selection and vendor/supplier selection. The results obtained by using current approach almost match with those of previous research works published in various open journals. The empirical study has demonstrated the simplicity and applicability of this method as a strategic decision making tool in a supply chain.

  14. Linguistic hesitant fuzzy multi-criteria decision-making method based on evidential reasoning

    Science.gov (United States)

    Zhou, Huan; Wang, Jian-qiang; Zhang, Hong-yu; Chen, Xiao-hong

    2016-01-01

    Linguistic hesitant fuzzy sets (LHFSs), which can be used to represent decision-makers' qualitative preferences as well as reflect their hesitancy and inconsistency, have attracted a great deal of attention due to their flexibility and efficiency. This paper focuses on a multi-criteria decision-making approach that combines LHFSs with the evidential reasoning (ER) method. After reviewing existing studies of LHFSs, a new order relationship and Hamming distance between LHFSs are introduced and some linguistic scale functions are applied. Then, the ER algorithm is used to aggregate the distributed assessment of each alternative. Subsequently, the set of aggregated alternatives on criteria are further aggregated to get the overall value of each alternative. Furthermore, a nonlinear programming model is developed and genetic algorithms are used to obtain the optimal weights of the criteria. Finally, two illustrative examples are provided to show the feasibility and usability of the method, and comparison analysis with the existing method is made.

  15. Using fuzzy-trace theory to understand and improve health judgments, decisions, and behaviors: A literature review.

    Science.gov (United States)

    Blalock, Susan J; Reyna, Valerie F

    2016-08-01

    Fuzzy-trace theory is a dual-process model of memory, reasoning, judgment, and decision making that contrasts with traditional expectancy-value approaches. We review the literature applying fuzzy-trace theory to health with 3 aims: evaluating whether the theory's basic distinctions have been validated empirically in the domain of health; determining whether these distinctions are useful in assessing, explaining, and predicting health-related psychological processes; and determining whether the theory can be used to improve health judgments, decisions, or behaviors, especially compared to other approaches. We conducted a literature review using PubMed, PsycINFO, and Web of Science to identify empirical peer-reviewed papers that applied fuzzy-trace theory, or central constructs of the theory, to investigate health judgments, decisions, or behaviors. Seventy nine studies (updated total is 94 studies; see Supplemental materials) were identified, over half published since 2012, spanning a wide variety of conditions and populations. Study findings supported the prediction that verbatim and gist representations are distinct constructs that can be retrieved independently using different cues. Although gist-based reasoning was usually associated with improved judgment and decision making, 4 sources of bias that can impair gist reasoning were identified. Finally, promising findings were reported from intervention studies that used fuzzy-trace theory to improve decision making and decrease unhealthy risk taking. Despite large gaps in the literature, most studies supported all 3 aims. By focusing on basic psychological processes that underlie judgment and decision making, fuzzy-trace theory provides insights into how individuals make decisions involving health risks and suggests innovative intervention approaches to improve health outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  16. Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia: necessary modifications

    Science.gov (United States)

    Al-Qudaimi, Abdullah; Kumar, Amit

    2018-05-01

    Recently, Abdullah and Najib (International Journal of Sustainable Energy 35(4): 360-377, 2016) proposed an intuitionistic fuzzy analytic hierarchy process to deal with uncertainty in decision-making and applied it to establish preference in the sustainable energy planning decision-making of Malaysia. This work may attract the researchers of other countries to choose energy technology for their countries. However, after a deep study of the published paper (International Journal of Sustainable Energy 35(4): 362-377, 2016), it is noticed that the expression used by Abdullah and Najib in Step 6 of their proposed method for evaluating the intuitionistic fuzzy entropy of each aggregate of each row of intuitionistic fuzzy matrix is not valid. Therefore, it is not genuine to use the method proposed by Abdullah and Najib for solving real-life problems. The aim of this paper was to suggest the required necessary modifications for resolving the flaws of the Abdullah and Najib method.

  17. Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression.

    Science.gov (United States)

    Chen, Guangchao; Li, Xuehua; Chen, Jingwen; Zhang, Ya-Nan; Peijnenburg, Willie J G M

    2014-12-01

    Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed. © 2014 SETAC.

  18. Optimization and analysis of decision trees and rules: Dynamic programming approach

    KAUST Repository

    Alkhalid, Abdulaziz

    2013-08-01

    This paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this systems work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction. © 2013 Taylor and Francis Group, LLC.

  19. Optimization and analysis of decision trees and rules: Dynamic programming approach

    KAUST Repository

    Alkhalid, Abdulaziz; Amin, Talha M.; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail; Zielosko, Beata

    2013-01-01

    This paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this systems work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction. © 2013 Taylor and Francis Group, LLC.

  20. Development of decision support system for employee selection using Adaptive Neuro Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    ‘Azzam Abdullah

    2018-01-01

    Full Text Available The number of children day care is increasing from year to year. Children day care is categorized as service industry that help parents in caring and educate children. This type of service industry plays a substitute for the family at certain hours, usually during work hours. The common problems in this industry is related to the employee performance. Most of employees have a less understanding about the whole job. Some employees only perform a routine task, i.e. feeding, cleaning and putting the child to sleep. The role in educating children is not performed as well as possible. Therefore, the employee selection is an important process to solve a children day care problem. An effective decision support system is required to optimize the employee selection process. Adaptive neuro fuzzy inference system (ANFIS is used to develop the decision support system for employee selection process. The data used to build the system is the historical data of employee selection process in children day care. The data shows the characteristic of job applicant that qualified and not qualified. From that data, the system can perform a learning process and give the right decision. The system is able to provide the right decision with an error of 0,00016249. It means that the decision support system that developed using ANFIS can give the right recommendation for employee selection process.

  1. A fuzzy-based reliability approach to evaluate basic events of fault tree analysis for nuclear power plant probabilistic safety assessment

    International Nuclear Information System (INIS)

    Purba, Julwan Hendry

    2014-01-01

    Highlights: • We propose a fuzzy-based reliability approach to evaluate basic event reliabilities. • It implements the concepts of failure possibilities and fuzzy sets. • Experts evaluate basic event failure possibilities using qualitative words. • Triangular fuzzy numbers mathematically represent qualitative failure possibilities. • It is a very good alternative for conventional reliability approach. - Abstract: Fault tree analysis has been widely utilized as a tool for nuclear power plant probabilistic safety assessment. This analysis can be completed only if all basic events of the system fault tree have their quantitative failure rates or failure probabilities. However, it is difficult to obtain those failure data due to insufficient data, environment changing or new components. This study proposes a fuzzy-based reliability approach to evaluate basic events of system fault trees whose failure precise probability distributions of their lifetime to failures are not available. It applies the concept of failure possibilities to qualitatively evaluate basic events and the concept of fuzzy sets to quantitatively represent the corresponding failure possibilities. To demonstrate the feasibility and the effectiveness of the proposed approach, the actual basic event failure probabilities collected from the operational experiences of the David–Besse design of the Babcock and Wilcox reactor protection system fault tree are used to benchmark the failure probabilities generated by the proposed approach. The results confirm that the proposed fuzzy-based reliability approach arises as a suitable alternative for the conventional probabilistic reliability approach when basic events do not have the corresponding quantitative historical failure data for determining their reliability characteristics. Hence, it overcomes the limitation of the conventional fault tree analysis for nuclear power plant probabilistic safety assessment

  2. Peripheral Exophytic Oral Lesions: A Clinical Decision Tree

    Directory of Open Access Journals (Sweden)

    Hamed Mortazavi

    2017-01-01

    Full Text Available Diagnosis of peripheral oral exophytic lesions might be quite challenging. This review article aimed to introduce a decision tree for oral exophytic lesions according to their clinical features. General search engines and specialized databases including PubMed, PubMed Central, Medline Plus, EBSCO, Science Direct, Scopus, Embase, and authenticated textbooks were used to find relevant topics by means of keywords such as “oral soft tissue lesion,” “oral tumor like lesion,” “oral mucosal enlargement,” and “oral exophytic lesion.” Related English-language articles published since 1988 to 2016 in both medical and dental journals were appraised. Upon compilation of data, peripheral oral exophytic lesions were categorized into two major groups according to their surface texture: smooth (mesenchymal or nonsquamous epithelium-originated and rough (squamous epithelium-originated. Lesions with smooth surface were also categorized into three subgroups according to their general frequency: reactive hyperplastic lesions/inflammatory hyperplasia, salivary gland lesions (nonneoplastic and neoplastic, and mesenchymal lesions (benign and malignant neoplasms. In addition, lesions with rough surface were summarized in six more common lesions. In total, 29 entities were organized in the form of a decision tree in order to help clinicians establish a logical diagnosis by a stepwise progression method.

  3. Decision Tree Approach to Discovering Fraud in Leasing Agreements

    Directory of Open Access Journals (Sweden)

    Horvat Ivan

    2014-09-01

    Full Text Available Background: Fraud attempts create large losses for financing subjects in modern economies. At the same time, leasing agreements have become more and more popular as a means of financing objects such as machinery and vehicles, but are more vulnerable to fraud attempts. Objectives: The goal of the paper is to estimate the usability of the data mining approach in discovering fraud in leasing agreements. Methods/Approach: Real-world data from one Croatian leasing firm was used for creating tow models for fraud detection in leasing. The decision tree method was used for creating a classification model, and the CHAID algorithm was deployed. Results: The decision tree model has indicated that the object of the leasing agreement had the strongest impact on the probability of fraud. Conclusions: In order to enhance the probability of the developed model, it would be necessary to develop software that would enable automated, quick and transparent retrieval of data from the system, processing according to the rules and displaying the results in multiple categories.

  4. Toward the Decision Tree for Inferring Requirements Maturation Types

    Science.gov (United States)

    Nakatani, Takako; Kondo, Narihito; Shirogane, Junko; Kaiya, Haruhiko; Hori, Shozo; Katamine, Keiichi

    Requirements are elicited step by step during the requirements engineering (RE) process. However, some types of requirements are elicited completely after the scheduled requirements elicitation process is finished. Such a situation is regarded as problematic situation. In our study, the difficulties of eliciting various kinds of requirements is observed by components. We refer to the components as observation targets (OTs) and introduce the word “Requirements maturation.” It means when and how requirements are elicited completely in the project. The requirements maturation is discussed on physical and logical OTs. OTs Viewed from a logical viewpoint are called logical OTs, e.g. quality requirements. The requirements of physical OTs, e.g., modules, components, subsystems, etc., includes functional and non-functional requirements. They are influenced by their requesters' environmental changes, as well as developers' technical changes. In order to infer the requirements maturation period of each OT, we need to know how much these factors influence the OTs' requirements maturation. According to the observation of actual past projects, we defined the PRINCE (Pre Requirements Intelligence Net Consideration and Evaluation) model. It aims to guide developers in their observation of the requirements maturation of OTs. We quantitatively analyzed the actual cases with their requirements elicitation process and extracted essential factors that influence the requirements maturation. The results of interviews of project managers are analyzed by WEKA, a data mining system, from which the decision tree was derived. This paper introduces the PRINCE model and the category of logical OTs to be observed. The decision tree that helps developers infer the maturation type of an OT is also described. We evaluate the tree through real projects and discuss its ability to infer the requirements maturation types.

  5. DECISION TREE CLASSIFIERS FOR STAR/GALAXY SEPARATION

    International Nuclear Information System (INIS)

    Vasconcellos, E. C.; Ruiz, R. S. R.; De Carvalho, R. R.; Capelato, H. V.; Gal, R. R.; LaBarbera, F. L.; Frago Campos Velho, H.; Trevisan, M.

    2011-01-01

    We study the star/galaxy classification efficiency of 13 different decision tree algorithms applied to photometric objects in the Sloan Digital Sky Survey Data Release Seven (SDSS-DR7). Each algorithm is defined by a set of parameters which, when varied, produce different final classification trees. We extensively explore the parameter space of each algorithm, using the set of 884,126 SDSS objects with spectroscopic data as the training set. The efficiency of star-galaxy separation is measured using the completeness function. We find that the Functional Tree algorithm (FT) yields the best results as measured by the mean completeness in two magnitude intervals: 14 ≤ r ≤ 21 (85.2%) and r ≥ 19 (82.1%). We compare the performance of the tree generated with the optimal FT configuration to the classifications provided by the SDSS parametric classifier, 2DPHOT, and Ball et al. We find that our FT classifier is comparable to or better in completeness over the full magnitude range 15 ≤ r ≤ 21, with much lower contamination than all but the Ball et al. classifier. At the faintest magnitudes (r > 19), our classifier is the only one that maintains high completeness (>80%) while simultaneously achieving low contamination (∼2.5%). We also examine the SDSS parametric classifier (psfMag - modelMag) to see if the dividing line between stars and galaxies can be adjusted to improve the classifier. We find that currently stars in close pairs are often misclassified as galaxies, and suggest a new cut to improve the classifier. Finally, we apply our FT classifier to separate stars from galaxies in the full set of 69,545,326 SDSS photometric objects in the magnitude range 14 ≤ r ≤ 21.

  6. Water supply management using an extended group fuzzy decision-making method: a case study in north-eastern Iran

    Science.gov (United States)

    Minatour, Yasser; Bonakdari, Hossein; Zarghami, Mahdi; Bakhshi, Maryam Ali

    2015-09-01

    The purpose of this study was to develop a group fuzzy multi-criteria decision-making method to be applied in rating problems associated with water resources management. Thus, here Chen's group fuzzy TOPSIS method extended by a difference technique to handle uncertainties of applying a group decision making. Then, the extended group fuzzy TOPSIS method combined with a consistency check. In the presented method, initially linguistic judgments are being surveyed via a consistency checking process, and afterward these judgments are being used in the extended Chen's fuzzy TOPSIS method. Here, each expert's opinion is turned to accurate mathematical numbers and, then, to apply uncertainties, the opinions of group are turned to fuzzy numbers using three mathematical operators. The proposed method is applied to select the optimal strategy for the rural water supply of Nohoor village in north-eastern Iran, as a case study and illustrated example. Sensitivity analyses test over results and comparing results with project reality showed that proposed method offered good results for water resources projects.

  7. Design of an Action Selection Mechanism for Cooperative Soccer Robots Based on Fuzzy Decision Making Algorithm

    Directory of Open Access Journals (Sweden)

    S. Alireza Mohades Kasaei

    2010-04-01

    Full Text Available Robocup is an international competition for multi agent research and related subject like: Artificial intelligence, Image processing, machine learning, robot path planning, control, and
    obstacle avoidance. In a soccer robot game, the environment is highly competitive and dynamic. In order to work in the dynamically changing environment, the decision-making system of a soccer robot system should have the features of flexibility and real-time adaptation. In this paper we will
    focus on the Middle Size Soccer Robot league (MSL and new hierarchical hybrid fuzzy methods for decision making and action selection of a robot in Middle Size Soccer Robot league (MSL are presented. First, the behaviors of an agent are introduced, implemented and classified in two layers,
    the Low_Level_Behaviors and the High_Level_Behaviors. In the second layer, a two phase mechanism for decision making is introduced. In phase one, some useful methods are implemented which check the robot’s situation for performing required behaviors. In the next phase, the team strategy, team formation, robot’s role and the robot’s positioning system are introduced. A fuzzy logical approach is employed to recognize the team strategy and further more to tell the player the
    best position to move. We believe that a Dynamic role engine is necessary for a successful team. Dynamic role engine and formation control during offensive or defensive play, help us to prevent collision avoidance among own players when attacking the ball and obstacle avoidance of the opponents. At last, we comprised our implemented algorithm in the Robocup 2007 and 2008 and results showed the efficiency of the introduced methodology. The results are satisfactory which has already been successfully implemented in ADRO RoboCup team. This project is still in progress and some new interesting methods are described in the current report.

  8. Assessment of hydrogen fuel cell applications using fuzzy multiple-criteria decision making method

    International Nuclear Information System (INIS)

    Chang, Pao-Long; Hsu, Chiung-Wen; Lin, Chiu-Yue

    2012-01-01

    Highlights: ► This study uses the fuzzy MCDM method to assess hydrogen fuel cell applications. ► We evaluate seven different hydrogen fuel cell applications based on 14 criteria. ► Results show that fuel cell backup power systems should be chosen for development in Taiwan. -- Abstract: Assessment is an essential process in framing government policy. It is critical to select the appropriate targets to meet the needs of national development. This study aimed to develop an assessment model for evaluating hydrogen fuel cell applications and thus provide a screening tool for decision makers. This model operates by selecting evaluation criteria, determining criteria weights, and assessing the performance of hydrogen fuel cell applications for each criterion. The fuzzy multiple-criteria decision making method was used to select the criteria and the preferred hydrogen fuel cell products based on information collected from a group of experts. Survey questionnaires were distributed to collect opinions from experts in different fields. After the survey, the criteria weights and a ranking of alternatives were obtained. The study first defined the evaluation criteria in terms of the stakeholders, so that comprehensive influence criteria could be identified. These criteria were then classified as environmental, technological, economic, or social to indicate the purpose of each criterion in the assessment process. The selected criteria included 14 indicators, such as energy efficiency and CO 2 emissions, as well as seven hydrogen fuel cell applications, such as forklifts and backup power systems. The results show that fuel cell backup power systems rank the highest, followed by household fuel cell electric-heat composite systems. The model provides a screening tool for decision makers to select hydrogen-related applications.

  9. Construction of admissible linear orders for interval-valued Atanassov intuitionistic fuzzy sets with an application to decision making

    Czech Academy of Sciences Publication Activity Database

    De Miguel, L.; Bustince, H.; Fernandez, J.; Indurain, E.; Kolesárová, A.; Mesiar, Radko

    2016-01-01

    Roč. 27, č. 1 (2016), s. 189-197 ISSN 1566-2535 Institutional support: RVO:67985556 Keywords : Mulit-expert decision making * Interval-valued Atanassov intuitionistic fuzzy set * Interval linear order Subject RIV: BA - General Mathematics Impact factor: 5.667, year: 2016 http://library.utia.cas.cz/separaty/2016/E/mesiar-0462471.pdf

  10. An integrated multi attribute decision model for energy efficiency processes in petrochemical industry applying fuzzy set theory

    International Nuclear Information System (INIS)

    Taylan, Osman; Kaya, Durmus; Demirbas, Ayhan

    2016-01-01

    Graphical abstract: Evaluation of compressors by comparing the different cost parameters. - Highlights: • Fuzzy sets and systems are used for decision making in MCDM problems. • An integrated Fuzzy AHP and fuzzy TOPSIS approaches are employed for compressor selection. • Compressor selection is a highly complex and non-linear process. • This approach increases the efficiency, reliability of alternative scenarios, and reduces the pay-back period. - Abstract: Energy efficient technologies offered by the market increases productivity. However, decision making for these technologies is usually obstructed in the firms and comes up with organizational barriers. Compressor selection in petrochemical industry requires assessment of several criteria such as ‘reliability, energy consumption, initial investment, capacity, pressure, and maintenance cost.’ Therefore, air compressor selection is a multi-attribute decision making (MADM) problem. The aim of this study is to select the most eligible compressor(s) so as to avoid the high energy consumption due to the capacity and maintenance costs. It is also aimed to avoid failures due to the reliability problems and high pressure. MADM usually takes place in a vague and imprecise environment. Soft computing techniques such as fuzzy sets and system can be used for decision making where vague and imprecise knowledge is available. In this study, an integrated fuzzy analytical hierarchy process (FAHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) methodologies are employed for the compressor selection. Fuzzy AHP was used to determine the weights of criteria and fuzzy TOPSIS was employed to order the scenarios according to their superiority. The total effect of all criteria was determined for all alternative scenarios to make an optimal decision. Moreover, the types of compressor, carbon emission, waste heat recovery and their capacities were analyzed and compared by statistical

  11. Decision-making methodology of optimal shielding materials by using fuzzy linear programming

    International Nuclear Information System (INIS)

    Kanai, Y.; Miura, T.; Hirao, Y.

    2000-01-01

    The main purpose of our studies are to select materials and determine the ratio of constituent materials as the first stage of optimum shielding design to suit the individual requirements of nuclear reactors, reprocessing facilities, casks for shipping spent fuel, etc. The parameters of the shield optimization are cost, space, weight and some shielding properties such as activation rates or individual irradiation and cooling time, and total dose rate for neutrons (including secondary gamma ray) and for primary gamma ray. Using conventional two-valued logic (i.e. crisp) approaches, huge combination calculations are needed to identify suitable materials for optimum shielding design. Also, re-computation is required for minor changes, as the approach does not react sensitively to the computation result. Present approach using a fuzzy linear programming method is much of the decision-making toward the satisfying solution might take place in fuzzy environment. And it can quickly and easily provide a guiding principle of optimal selection of shielding materials under the above-mentioned conditions. The possibility or reducing radiation effects by optimizing the ratio of constituent materials is investigated. (author)

  12. Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making methods

    CERN Document Server

    Rao, Ravipudi Venkata

    2007-01-01

    Manufacturing is the backbone of any industrialized nation. Recent worldwide advances in manufacturing technologies have brought about a metamorphism in the industry. Fast-changing technologies on the product front have created a need for an equally fast response from manufacturing industries. To meet these challenges, manufacturing industries have to select appropriate manufacturing strategies, product designs, manufacturing processes, work piece and tool materials, and machinery and equipment. The selection decisions are complex as decision making is more challenging today. Decision makers i

  13. Coronal Mass Ejection Data Clustering and Visualization of Decision Trees

    Science.gov (United States)

    Ma, Ruizhe; Angryk, Rafal A.; Riley, Pete; Filali Boubrahimi, Soukaina

    2018-05-01

    Coronal mass ejections (CMEs) can be categorized as either “magnetic clouds” (MCs) or non-MCs. Features such as a large magnetic field, low plasma-beta, and low proton temperature suggest that a CME event is also an MC event; however, so far there is neither a definitive method nor an automatic process to distinguish the two. Human labeling is time-consuming, and results can fluctuate owing to the imprecise definition of such events. In this study, we approach the problem of MC and non-MC distinction from a time series data analysis perspective and show how clustering can shed some light on this problem. Although many algorithms exist for traditional data clustering in the Euclidean space, they are not well suited for time series data. Problems such as inadequate distance measure, inaccurate cluster center description, and lack of intuitive cluster representations need to be addressed for effective time series clustering. Our data analysis in this work is twofold: clustering and visualization. For clustering we compared the results from the popular hierarchical agglomerative clustering technique to a distance density clustering heuristic we developed previously for time series data clustering. In both cases, dynamic time warping will be used for similarity measure. For classification as well as visualization, we use decision trees to aggregate single-dimensional clustering results to form a multidimensional time series decision tree, with averaged time series to present each decision. In this study, we achieved modest accuracy and, more importantly, an intuitive interpretation of how different parameters contribute to an MC event.

  14. How Reasoning, Judgment, and Decision Making are Colored by Gist-based Intuition: A Fuzzy-Trace Theory Approach.

    Science.gov (United States)

    Corbin, Jonathan C; Reyna, Valerie F; Weldon, Rebecca B; Brainerd, Charles J

    2015-12-01

    Fuzzy-trace theory distinguishes verbatim (literal, exact) from gist (meaningful) representations, predicting that reliance on gist increases with experience and expertise. Thus, many judgment-and-decision-making biases increase with development, such that cognition is colored by context in ways that violate logical coherence and probability theories. Nevertheless, this increase in gist-based intuition is adaptive: Gist is stable, less sensitive to interference, and easier to manipulate. Moreover, gist captures the functionally significant essence of information, supporting healthier and more robust decision processes. We describe how fuzzy-trace theory accounts for judgment-and-decision making phenomena, predicting the paradoxical arc of these processes with the development of experience and expertise. We present data linking gist memory processes to gist processing in decision making and provide illustrations of gist reliance in medicine, public health, and intelligence analysis.

  15. A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information.

    Science.gov (United States)

    Bai, Yu-Ting; Zhang, Bai-Hai; Wang, Xiao-Yi; Jin, Xue-Bo; Xu, Ji-Ping; Su, Ting-Li; Wang, Zhao-Yang

    2016-10-28

    Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches' ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method's rationality and feasibility when using different data from different sources.

  16. A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information

    Directory of Open Access Journals (Sweden)

    Yu-Ting Bai

    2016-10-01

    Full Text Available Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches’ ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method’s rationality and feasibility when using different data from different sources.

  17. A new approach to enhance the performance of decision tree for classifying gene expression data.

    Science.gov (United States)

    Hassan, Md; Kotagiri, Ramamohanarao

    2013-12-20

    Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset. By using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature. We experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.

  18. Bi-Criteria Optimization of Decision Trees with Applications to Data Analysis

    KAUST Repository

    Chikalov, Igor

    2017-10-19

    This paper is devoted to the study of bi-criteria optimization problems for decision trees. We consider different cost functions such as depth, average depth, and number of nodes. We design algorithms that allow us to construct the set of Pareto optimal points (POPs) for a given decision table and the corresponding bi-criteria optimization problem. These algorithms are suitable for investigation of medium-sized decision tables. We discuss three examples of applications of the created tools: the study of relationships among depth, average depth and number of nodes for decision trees for corner point detection (such trees are used in computer vision for object tracking), study of systems of decision rules derived from decision trees, and comparison of different greedy algorithms for decision tree construction as single- and bi-criteria optimization algorithms.

  19. Data Fusion Research of Triaxial Human Body Motion Gesture based on Decision Tree

    Directory of Open Access Journals (Sweden)

    Feihong Zhou

    2014-05-01

    Full Text Available The development status of human body motion gesture data fusion domestic and overseas has been analyzed. A triaxial accelerometer is adopted to develop a wearable human body motion gesture monitoring system aimed at old people healthcare. On the basis of a brief introduction of decision tree algorithm, the WEKA workbench is adopted to generate a human body motion gesture decision tree. At last, the classification quality of the decision tree has been validated through experiments. The experimental results show that the decision tree algorithm could reach an average predicting accuracy of 97.5 % with lower time cost.

  20. Using histograms to introduce randomization in the generation of ensembles of decision trees

    Science.gov (United States)

    Kamath, Chandrika; Cantu-Paz, Erick; Littau, David

    2005-02-22

    A system for decision tree ensembles that includes a module to read the data, a module to create a histogram, a module to evaluate a potential split according to some criterion using the histogram, a module to select a split point randomly in an interval around the best split, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method includes the steps of reading the data; creating a histogram; evaluating a potential split according to some criterion using the histogram, selecting a split point randomly in an interval around the best split, splitting the data, and combining multiple decision trees in ensembles.

  1. Bayesian averaging over Decision Tree models for trauma severity scoring.

    Science.gov (United States)

    Schetinin, V; Jakaite, L; Krzanowski, W

    2018-01-01

    Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Development of an OLAP Based Fuzzy Logic System for Supporting Put Away Decision

    Directory of Open Access Journals (Sweden)

    S.H.Chung

    2009-10-01

    Full Text Available In today`s rapidly changing and globally volatile world, manufacturers pay strong efforts on conducting lean production, outsourcing their components, and management on the complex supply chain. Warehouse management plays a vital role to be a successful player in the any kinds of industry which put-away process is a key activity that brings significant influence and challenges to warehouse performance. In this dynamic operating environment, minimizing the operation mistakes and providing accurate real time inventory information to stakeholder become the basic requirements to be an order qualifier. An OLAP based intelligent system called Fuzzy Storage Assignment System (FSAS is proposed to increase availability of decision support data and convert the human knowledge into system for tackling the storage location assignment problem (SLAP. To validate the feasibility of this proposed system, a prototype will be worked out for a third party logistics company.

  3. Fuzzy decision-making: a new method in model selection via various validity criteria

    International Nuclear Information System (INIS)

    Shakouri Ganjavi, H.; Nikravesh, K.

    2001-01-01

    Modeling is considered as the first step in scientific investigations. Several alternative models may be candida ted to express a phenomenon. Scientists use various criteria to select one model between the competing models. Based on the solution of a Fuzzy Decision-Making problem, this paper proposes a new method in model selection. The method enables the scientist to apply all desired validity criteria, systematically by defining a proper Possibility Distribution Function due to each criterion. Finally, minimization of a utility function composed of the Possibility Distribution Functions will determine the best selection. The method is illustrated through a modeling example for the A verage Daily Time Duration of Electrical Energy Consumption in Iran

  4. On the analytic hierarchy process and decision support based on fuzzy-linguistic preference structures

    DEFF Research Database (Denmark)

    Franco de los Rios, Camilo Andres

    2014-01-01

    , where experts value pairs of alternatives/criteria with words, making it essentially fuzzy under the view that words can be represented by fuzzy sets for their respective computation. Hence, reasoning with fuzzy logic is justified by the analytical framework that it offers to design the meaning of words...

  5. Multi-criteria multi-stakeholder decision analysis using a fuzzy-stochastic approach for hydrosystem management

    Science.gov (United States)

    Subagadis, Y. H.; Schütze, N.; Grundmann, J.

    2014-09-01

    The conventional methods used to solve multi-criteria multi-stakeholder problems are less strongly formulated, as they normally incorporate only homogeneous information at a time and suggest aggregating objectives of different decision-makers avoiding water-society interactions. In this contribution, Multi-Criteria Group Decision Analysis (MCGDA) using a fuzzy-stochastic approach has been proposed to rank a set of alternatives in water management decisions incorporating heterogeneous information under uncertainty. The decision making framework takes hydrologically, environmentally, and socio-economically motivated conflicting objectives into consideration. The criteria related to the performance of the physical system are optimized using multi-criteria simulation-based optimization, and fuzzy linguistic quantifiers have been used to evaluate subjective criteria and to assess stakeholders' degree of optimism. The proposed methodology is applied to find effective and robust intervention strategies for the management of a coastal hydrosystem affected by saltwater intrusion due to excessive groundwater extraction for irrigated agriculture and municipal use. Preliminary results show that the MCGDA based on a fuzzy-stochastic approach gives useful support for robust decision-making and is sensitive to the decision makers' degree of optimism.

  6. Multi-criteria multi-stakeholder decision analysis using a fuzzy-stochastic approach for hydrosystem management

    Directory of Open Access Journals (Sweden)

    Y. H. Subagadis

    2014-09-01

    Full Text Available The conventional methods used to solve multi-criteria multi-stakeholder problems are less strongly formulated, as they normally incorporate only homogeneous information at a time and suggest aggregating objectives of different decision-makers avoiding water–society interactions. In this contribution, Multi-Criteria Group Decision Analysis (MCGDA using a fuzzy-stochastic approach has been proposed to rank a set of alternatives in water management decisions incorporating heterogeneous information under uncertainty. The decision making framework takes hydrologically, environmentally, and socio-economically motivated conflicting objectives into consideration. The criteria related to the performance of the physical system are optimized using multi-criteria simulation-based optimization, and fuzzy linguistic quantifiers have been used to evaluate subjective criteria and to assess stakeholders' degree of optimism. The proposed methodology is applied to find effective and robust intervention strategies for the management of a coastal hydrosystem affected by saltwater intrusion due to excessive groundwater extraction for irrigated agriculture and municipal use. Preliminary results show that the MCGDA based on a fuzzy-stochastic approach gives useful support for robust decision-making and is sensitive to the decision makers' degree of optimism.

  7. Electronic Nose Odor Classification with Advanced Decision Tree Structures

    Directory of Open Access Journals (Sweden)

    S. Guney

    2013-09-01

    Full Text Available Electronic nose (e-nose is an electronic device which can measure chemical compounds in air and consequently classify different odors. In this paper, an e-nose device consisting of 8 different gas sensors was designed and constructed. Using this device, 104 different experiments involving 11 different odor classes (moth, angelica root, rose, mint, polis, lemon, rotten egg, egg, garlic, grass, and acetone were performed. The main contribution of this paper is the finding that using the chemical domain knowledge it is possible to train an accurate odor classification system. The domain knowledge about chemical compounds is represented by a decision tree whose nodes are composed of classifiers such as Support Vector Machines and k-Nearest Neighbor. The overall accuracy achieved with the proposed algorithm and the constructed e-nose device was 97.18 %. Training and testing data sets used in this paper are published online.

  8. Career Path Suggestion using String Matching and Decision Trees

    Science.gov (United States)

    Nagpal, Akshay; P. Panda, Supriya

    2015-05-01

    High school and college graduates seemingly are often battling for the courses they should major in order to achieve their target career. In this paper, we worked on suggesting a career path to a graduate to reach his/her dream career given the current educational status. Firstly, we collected the career data of professionals and academicians from various career fields and compiled the data set by using the necessary information from the data. Further, this was used as the basis to suggest the most appropriate career path for the person given his/her current educational status. Decision trees and string matching algorithms were employed to suggest the appropriate career path for a person. Finally, an analysis of the result has been done directing to further improvements in the model.

  9. Ship Engine Room Casualty Analysis by Using Decision Tree Method

    Directory of Open Access Journals (Sweden)

    Ömür Yaşar SAATÇİOĞLU

    2017-03-01

    Full Text Available Ships may encounter undesirable conditions during operations. In consequence of a casualty, fire, explosion, flooding, grounding, injury even death may occur. Besides, these results can be avoidable with precautions and preventive operating processes. In maritime transportation, casualties depend on various factors. These were listed as misuse of the engine equipment and tools, defective machinery or equipment, inadequacy of operational procedure and measure of safety and force majeure effects. Casualty reports which were published in Australia, New Zealand, United Kingdom, Canada and United States until 2015 were examined and the probable causes and consequences of casualties were determined with their occurrence percentages. In this study, 89 marine investigation reports regarding engine room casualties were analyzed. Casualty factors were analyzed with their frequency percentages and also their main causes were constructed. This study aims to investigate engine room based casualties, frequency of each casualty type and main causes by using decision tree method.

  10. A New Method of Multiattribute Decision-Making Based on Interval-Valued Hesitant Fuzzy Soft Sets and Its Application

    Directory of Open Access Journals (Sweden)

    Yan Yang

    2017-01-01

    Full Text Available Combining interval-valued hesitant fuzzy soft sets (IVHFSSs and a new comparative law, we propose a new method, which can effectively solve multiattribute decision-making (MADM problems. Firstly, a characteristic function of two interval values and a new comparative law of interval-valued hesitant fuzzy elements (IVHFEs based on the possibility degree are proposed. Then, we define two important definitions of IVHFSSs including the interval-valued hesitant fuzzy soft quasi subset and soft quasi equal based on the new comparative law. Finally, an algorithm is presented to solve MADM problems. We also use the method proposed in this paper to evaluate the importance of major components of the well drilling mud pump.

  11. Intracranial hypertension prediction using extremely randomized decision trees.

    Science.gov (United States)

    Scalzo, Fabien; Hamilton, Robert; Asgari, Shadnaz; Kim, Sunghan; Hu, Xiao

    2012-10-01

    Intracranial pressure (ICP) elevation (intracranial hypertension, IH) in neurocritical care is typically treated in a reactive fashion; it is only delivered after bedside clinicians notice prolonged ICP elevation. A proactive solution is desirable to improve the treatment of intracranial hypertension. Several studies have shown that the waveform morphology of the intracranial pressure pulse holds predictors about future intracranial hypertension and could therefore be used to alert the bedside clinician of a likely occurrence of the elevation in the immediate future. In this paper, a computational framework is proposed to predict prolonged intracranial hypertension based on morphological waveform features computed from the ICP. A key contribution of this work is to exploit an ensemble classifier method based on extremely randomized decision trees (Extra-Trees). Experiments on a representative set of 30 patients admitted for various intracranial pressure related conditions demonstrate the effectiveness of the predicting framework on ICP pulses acquired under clinical conditions and the superior results of the proposed approach in comparison to linear and AdaBoost classifiers. Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

  12. Extensions of Dynamic Programming: Decision Trees, Combinatorial Optimization, and Data Mining

    KAUST Repository

    Hussain, Shahid

    2016-01-01

    This thesis is devoted to the development of extensions of dynamic programming to the study of decision trees. The considered extensions allow us to make multi-stage optimization of decision trees relative to a sequence of cost functions, to count the number of optimal trees, and to study relationships: cost vs cost and cost vs uncertainty for decision trees by construction of the set of Pareto-optimal points for the corresponding bi-criteria optimization problem. The applications include study of totally optimal (simultaneously optimal relative to a number of cost functions) decision trees for Boolean functions, improvement of bounds on complexity of decision trees for diagnosis of circuits, study of time and memory trade-off for corner point detection, study of decision rules derived from decision trees, creation of new procedure (multi-pruning) for construction of classifiers, and comparison of heuristics for decision tree construction. Part of these extensions (multi-stage optimization) was generalized to well-known combinatorial optimization problems: matrix chain multiplication, binary search trees, global sequence alignment, and optimal paths in directed graphs.

  13. Extensions of Dynamic Programming: Decision Trees, Combinatorial Optimization, and Data Mining

    KAUST Repository

    Hussain, Shahid

    2016-07-10

    This thesis is devoted to the development of extensions of dynamic programming to the study of decision trees. The considered extensions allow us to make multi-stage optimization of decision trees relative to a sequence of cost functions, to count the number of optimal trees, and to study relationships: cost vs cost and cost vs uncertainty for decision trees by construction of the set of Pareto-optimal points for the corresponding bi-criteria optimization problem. The applications include study of totally optimal (simultaneously optimal relative to a number of cost functions) decision trees for Boolean functions, improvement of bounds on complexity of decision trees for diagnosis of circuits, study of time and memory trade-off for corner point detection, study of decision rules derived from decision trees, creation of new procedure (multi-pruning) for construction of classifiers, and comparison of heuristics for decision tree construction. Part of these extensions (multi-stage optimization) was generalized to well-known combinatorial optimization problems: matrix chain multiplication, binary search trees, global sequence alignment, and optimal paths in directed graphs.

  14. Tackling Complex Emergency Response Solutions Evaluation Problems in Sustainable Development by Fuzzy Group Decision Making Approaches with Considering Decision Hesitancy and Prioritization among Assessing Criteria.

    Science.gov (United States)

    Qi, Xiao-Wen; Zhang, Jun-Ling; Zhao, Shu-Ping; Liang, Chang-Yong

    2017-10-02

    In order to be prepared against potential balance-breaking risks affecting economic development, more and more countries have recognized emergency response solutions evaluation (ERSE) as an indispensable activity in their governance of sustainable development. Traditional multiple criteria group decision making (MCGDM) approaches to ERSE have been facing simultaneous challenging characteristics of decision hesitancy and prioritization relations among assessing criteria, due to the complexity in practical ERSE problems. Therefore, aiming at the special type of ERSE problems that hold the two characteristics, we investigate effective MCGDM approaches by hiring interval-valued dual hesitant fuzzy set (IVDHFS) to comprehensively depict decision hesitancy. To exploit decision information embedded in prioritization relations among criteria, we firstly define an fuzzy entropy measure for IVDHFS so that its derivative decision models can avoid potential information distortion in models based on classic IVDHFS distance measures with subjective supplementing mechanism; further, based on defined entropy measure, we develop two fundamental prioritized operators for IVDHFS by extending Yager's prioritized operators. Furthermore, on the strength of above methods, we construct two hesitant fuzzy MCGDM approaches to tackle complex scenarios with or without known weights for decision makers, respectively. Finally, case studies have been conducted to show effectiveness and practicality of our proposed approaches.

  15. Tackling Complex Emergency Response Solutions Evaluation Problems in Sustainable Development by Fuzzy Group Decision Making Approaches with Considering Decision Hesitancy and Prioritization among Assessing Criteria

    Directory of Open Access Journals (Sweden)

    Xiao-Wen Qi

    2017-10-01

    Full Text Available In order to be prepared against potential balance-breaking risks affecting economic development, more and more countries have recognized emergency response solutions evaluation (ERSE as an indispensable activity in their governance of sustainable development. Traditional multiple criteria group decision making (MCGDM approaches to ERSE have been facing simultaneous challenging characteristics of decision hesitancy and prioritization relations among assessing criteria, due to the complexity in practical ERSE problems. Therefore, aiming at the special type of ERSE problems that hold the two characteristics, we investigate effective MCGDM approaches by hiring interval-valued dual hesitant fuzzy set (IVDHFS to comprehensively depict decision hesitancy. To exploit decision information embedded in prioritization relations among criteria, we firstly define an fuzzy entropy measure for IVDHFS so that its derivative decision models can avoid potential information distortion in models based on classic IVDHFS distance measures with subjective supplementing mechanism; further, based on defined entropy measure, we develop two fundamental prioritized operators for IVDHFS by extending Yager’s prioritized operators. Furthermore, on the strength of above methods, we construct two hesitant fuzzy MCGDM approaches to tackle complex scenarios with or without known weights for decision makers, respectively. Finally, case studies have been conducted to show effectiveness and practicality of our proposed approaches.

  16. Evaluation of End-Products in Architecture Design Process: A Fuzzy Decision-Making Model

    Directory of Open Access Journals (Sweden)

    Serkan PALABIYIK

    2012-06-01

    Full Text Available This paper presents a study on the development of a fuzzy multi-criteria decision-making model for the evaluation of end products of the architectural design process. Potentials of the developed model were investigated within the scope of architectural design education, specifically an international design studio titled “Design for Disassembly and Reuse: Design & Building Multipurpose Transformable Pavilions.” The studio work followed a design process that integrated systematic and heuristic thinking. The design objectives and assessment criteria were clearly set out at the beginning of the process by the studio coordinator with the aim of narrowing the design space and increasing awareness of the consequences of design decisions. At the end of the design process, designs produced in the studio were evaluated using the developed model to support decision making. The model facilitated the identification of positive and negative aspects of the designs and selection of the design alternative that best met the studio objectives set at the beginning.

  17. Application fuzzy multi-attribute decision analysis method to prioritize project success criteria

    Science.gov (United States)

    Phong, Nguyen Thanh; Quyen, Nguyen Le Hoang Thuy To

    2017-11-01

    Project success is a foundation for project owner to manage and control not only for the current project but also for future potential projects in construction companies. However, identifying the key success criteria for evaluating a particular project in real practice is a challenging task. Normally, it depends on a lot of factors, such as the expectation of the project owner and stakeholders, triple constraints of the project (cost, time, quality), and company's mission, vision, and objectives. Traditional decision-making methods for measuring the project success are usually based on subjective opinions of panel experts, resulting in irrational and inappropriate decisions. Therefore, this paper introduces a multi-attribute decision analysis method (MADAM) for weighting project success criteria by using fuzzy Analytical Hierarchy Process approach. It is found that this method is useful when dealing with imprecise and uncertain human judgments in evaluating project success criteria. Moreover, this research also suggests that although cost, time, and quality are three project success criteria projects, the satisfaction of project owner and acceptance of project stakeholders with the completed project criteria is the most important criteria for project success evaluation in Vietnam.

  18. The value of decision tree analysis in planning anaesthetic care in obstetrics.

    Science.gov (United States)

    Bamber, J H; Evans, S A

    2016-08-01

    The use of decision tree analysis is discussed in the context of the anaesthetic and obstetric management of a young pregnant woman with joint hypermobility syndrome with a history of insensitivity to local anaesthesia and a previous difficult intubation due to a tongue tumour. The multidisciplinary clinical decision process resulted in the woman being delivered without complication by elective caesarean section under general anaesthesia after an awake fibreoptic intubation. The decision process used is reviewed and compared retrospectively to a decision tree analytical approach. The benefits and limitations of using decision tree analysis are reviewed and its application in obstetric anaesthesia is discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Binary Decision Tree Development for Probabilistic Safety Assessment Applications

    International Nuclear Information System (INIS)

    Simic, Z.; Banov, R.; Mikulicic, V.

    2008-01-01

    The aim of this article is to describe state of the development for the relatively new approach in the probabilistic safety analysis (PSA). This approach is based on the application of binary decision diagrams (BDD) representation for the logical function on the quantitative and qualitative analysis of complex systems that are presented by fault trees and event trees in the PSA applied for the nuclear power plants risk determination. Even BDD approach offers full solution comparing to the partial one from the conventional quantification approach there are still problems to be solved before new approach could be fully implemented. Major problem with full application of BDD is difficulty of getting any solution for the PSA models of certain complexity. This paper is comparing two approaches in PSA quantification. Major focus of the paper is description of in-house developed BDD application with implementation of the original algorithms. Resulting number of nodes required to represent the BDD is extremely sensitive to the chosen order of variables (i.e., basic events in PSA). The problem of finding an optimal order of variables that form the BDD falls under the class of NP-complete complexity. This paper presents an original approach to the problem of finding the initial order of variables utilized for the BDD construction by various dynamical reordering schemes. Main advantage of this approach compared to the known methods of finding the initial order is with better results in respect to the required working memory and time needed to finish the BDD construction. Developed method is compared against results from well known methods such as depth-first, breadth-first search procedures. Described method may be applied in finding of an initial order for fault trees/event trees being created from basic events by means of logical operations (e.g. negation, and, or, exclusive or). With some testing models a significant reduction of used memory has been achieved, sometimes

  20. Application of a collaborative modelling and strategic fuzzy decision support system for selecting appropriate resilience strategies for seaport operations

    Directory of Open Access Journals (Sweden)

    Andrew John

    2014-06-01

    Full Text Available The selection of an appropriate resilience investment strategy to optimize the operational efficiency of a seaport is a challenging task given that many criteria need to be considered and modelled under an uncertain environment. The design of such a complex decision system consists of many subjective and imprecise parameters contained in different quantitative and qualitative forms. This paper proposes a fuzzy multi-attribute decision making methodology for the selection of an appropriate resilience investment strategy in a succinct and straightforward manner. The decision support model allows for a collaborative modelling of the system by multiple analysts in a group decision making process. Fuzzy analytical hierarchy process (FAHP was utilized to analyse the complex structure of the system to obtain the weights of all the criteria while fuzzy technique for order of preference by similarity to ideal solution (TOPSIS was employed to facilitate the ranking process of the resilience strategies. Given that it is often financially difficult to invest in all the resilience strategies, it is envisaged that the proposed approach could provide decision makers with a flexible and transparent tool for selecting appropriate resilience strategies aimed at increasing the resilience of seaport operations.

  1. The integration methods of fuzzy fault mode and effect analysis and fault tree analysis for risk analysis of yogurt production

    Science.gov (United States)

    Aprilia, Ayu Rizky; Santoso, Imam; Ekasari, Dhita Murita

    2017-05-01

    Yogurt is a product based on milk, which has beneficial effects for health. The process for the production of yogurt is very susceptible to failure because it involves bacteria and fermentation. For an industry, the risks may cause harm and have a negative impact. In order for a product to be successful and profitable, it requires the analysis of risks that may occur during the production process. Risk analysis can identify the risks in detail and prevent as well as determine its handling, so that the risks can be minimized. Therefore, this study will analyze the risks of the production process with a case study in CV.XYZ. The method used in this research is the Fuzzy Failure Mode and Effect Analysis (fuzzy FMEA) and Fault Tree Analysis (FTA). The results showed that there are 6 risks from equipment variables, raw material variables, and process variables. Those risks include the critical risk, which is the risk of a lack of an aseptic process, more specifically if starter yogurt is damaged due to contamination by fungus or other bacteria and a lack of sanitation equipment. The results of quantitative analysis of FTA showed that the highest probability is the probability of the lack of an aseptic process, with a risk of 3.902%. The recommendations for improvement include establishing SOPs (Standard Operating Procedures), which include the process, workers, and environment, controlling the starter of yogurt and improving the production planning and sanitation equipment using hot water immersion.

  2. A Heuristic T-S Fuzzy Model for the Pumped-Storage Generator-Motor Using Variable-Length Tree-Seed Algorithm-Based Competitive Agglomeration

    Directory of Open Access Journals (Sweden)

    Jianzhong Zhou

    2018-04-01

    Full Text Available With the fast development of artificial intelligence techniques, data-driven modeling approaches are becoming hotspots in both academic research and engineering practice. This paper proposes a novel data-driven T-S fuzzy model to precisely describe the complicated dynamic behaviors of pumped storage generator motor (PSGM. In premise fuzzy partition of the proposed T-S fuzzy model, a novel variable-length tree-seed algorithm based competitive agglomeration (VTSA-CA algorithm is presented to determine the optimal number of clusters automatically and improve the fuzzy clustering performances. Besides, in order to promote modeling accuracy of PSGM, the input and output formats in the T-S fuzzy model are selected by an economical parameter controlled auto-regressive (CAR model derived from a high-order transfer function of PSGM considering the distributed components in the water diversion system of the power plant. The effectiveness and superiority of the T-S fuzzy model for PSGM under different working conditions are validated by performing comparative studies with both practical data and the conventional mechanistic model.

  3. Optimal Decision-Making in Fuzzy Economic Order Quantity (EOQ Model under Restricted Space: A Non-Linear Programming Approach

    Directory of Open Access Journals (Sweden)

    M. Pattnaik

    2013-08-01

    Full Text Available In this paper the concept of fuzzy Non-Linear Programming Technique is applied to solve an economic order quantity (EOQ model under restricted space. Since various types of uncertainties and imprecision are inherent in real inventory problems they are classically modeled using the approaches from the probability theory. However, there are uncertainties that cannot be appropriately treated by usual probabilistic models. The questions how to define inventory optimization tasks in such environment how to interpret optimal solutions arise. This paper allows the modification of the Single item EOQ model in presence of fuzzy decision making process where demand is related to the unit price and the setup cost varies with the quantity produced/Purchased. This paper considers the modification of objective function and storage area in the presence of imprecisely estimated parameters. The model is developed for the problem by employing different modeling approaches over an infinite planning horizon. It incorporates all concepts of a fuzzy arithmetic approach, the quantity ordered and the demand per unit compares both fuzzy non linear and other models. Investigation of the properties of an optimal solution allows developing an algorithm whose validity is illustrated through an example problem and ugh MATLAB (R2009a version software, the two and three dimensional diagrams are represented to the application. Sensitivity analysis of the optimal solution is also studied with respect to changes in different parameter values and to draw managerial insights of the decision problem.

  4. A novel method of fuzzy fault tree analysis combined with VB program to identify and assess the risk of coal dust explosions.

    Directory of Open Access Journals (Sweden)

    Hetang Wang

    Full Text Available Coal dust explosions (CDE are one of the main threats to the occupational safety of coal miners. Aiming to identify and assess the risk of CDE, this paper proposes a novel method of fuzzy fault tree analysis combined with the Visual Basic (VB program. In this methodology, various potential causes of the CDE are identified and a CDE fault tree is constructed. To overcome drawbacks from the lack of exact probability data for the basic events, fuzzy set theory is employed and the probability data of each basic event is treated as intuitionistic trapezoidal fuzzy numbers. In addition, a new approach for calculating the weighting of each expert is also introduced in this paper to reduce the error during the expert elicitation process. Specifically, an in-depth quantitative analysis of the fuzzy fault tree, such as the importance measure of the basic events and the cut sets, and the CDE occurrence probability is given to assess the explosion risk and acquire more details of the CDE. The VB program is applied to simplify the analysis process. A case study and analysis is provided to illustrate the effectiveness of this proposed method, and some suggestions are given to take preventive measures in advance and avoid CDE accidents.

  5. The new decision tree for the evaluation of pesticide leaching from soils

    NARCIS (Netherlands)

    Linden AMA van der; Boesten JJTI; Cornelese AA; Kruijne R; Leistra M; Linders JBHJ; Pol JW; Tiktak A; Verschoor AJ; Alterra; CTB; LDL; SEC; LER; Alterra

    2004-01-01

    The Dutch decision tree on leaching from soil has been re-designed to be more in line with EU guidelines on the assessment of the leaching potential of substances. The new decision tree explicitly defines reasonable worst-case conditions as the 90th percentile of the area to which a substance is

  6. An Improved Decision Tree for Predicting a Major Product in Competing Reactions

    Science.gov (United States)

    Graham, Kate J.

    2014-01-01

    When organic chemistry students encounter competing reactions, they are often overwhelmed by the task of evaluating multiple factors that affect the outcome of a reaction. The use of a decision tree is a useful tool to teach students to evaluate a complex situation and propose a likely outcome. Specifically, a decision tree can help students…

  7. Multi-pruning of decision trees for knowledge representation and classification

    KAUST Repository

    Azad, Mohammad

    2016-06-09

    We consider two important questions related to decision trees: first how to construct a decision tree with reasonable number of nodes and reasonable number of misclassification, and second how to improve the prediction accuracy of decision trees when they are used as classifiers. We have created a dynamic programming based approach for bi-criteria optimization of decision trees relative to the number of nodes and the number of misclassification. This approach allows us to construct the set of all Pareto optimal points and to derive, for each such point, decision trees with parameters corresponding to that point. Experiments on datasets from UCI ML Repository show that, very often, we can find a suitable Pareto optimal point and derive a decision tree with small number of nodes at the expense of small increment in number of misclassification. Based on the created approach we have proposed a multi-pruning procedure which constructs decision trees that, as classifiers, often outperform decision trees constructed by CART. © 2015 IEEE.

  8. Constructing multi-labelled decision trees for junction design using the predicted probabilities

    NARCIS (Netherlands)

    Bezembinder, Erwin M.; Wismans, Luc J. J.; Van Berkum, Eric C.

    2017-01-01

    In this paper, we evaluate the use of traditional decision tree algorithms CRT, CHAID and QUEST to determine a decision tree which can be used to predict a set of (Pareto optimal) junction design alternatives (e.g. signal or roundabout) for a given traffic demand pattern and available space. This is

  9. Decision-Tree Models of Categorization Response Times, Choice Proportions, and Typicality Judgments

    Science.gov (United States)

    Lafond, Daniel; Lacouture, Yves; Cohen, Andrew L.

    2009-01-01

    The authors present 3 decision-tree models of categorization adapted from T. Trabasso, H. Rollins, and E. Shaughnessy (1971) and use them to provide a quantitative account of categorization response times, choice proportions, and typicality judgments at the individual-participant level. In Experiment 1, the decision-tree models were fit to…

  10. A Decision Tree for Psychology Majors: Supplying Questions as Well as Answers.

    Science.gov (United States)

    Poe, Retta E.

    1988-01-01

    Outlines the development of a psychology careers decision tree to help faculty advise students plan their program. States that students using the decision tree may benefit by learning more about their career options and by acquiring better question-asking skills. (GEA)

  11. A Branch-and-Price approach to find optimal decision trees

    NARCIS (Netherlands)

    Firat, M.; Crognier, Guillaume; Gabor, Adriana; Zhang, Y.

    2018-01-01

    In Artificial Intelligence (AI) field, decision trees have gained certain importance due to their effectiveness in solving classification and regression problems. Recently, in the literature we see finding optimal decision trees are formulated as Mixed Integer Linear Programming (MILP) models. This

  12. Multi-pruning of decision trees for knowledge representation and classification

    KAUST Repository

    Azad, Mohammad; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2016-01-01

    We consider two important questions related to decision trees: first how to construct a decision tree with reasonable number of nodes and reasonable number of misclassification, and second how to improve the prediction accuracy of decision trees when they are used as classifiers. We have created a dynamic programming based approach for bi-criteria optimization of decision trees relative to the number of nodes and the number of misclassification. This approach allows us to construct the set of all Pareto optimal points and to derive, for each such point, decision trees with parameters corresponding to that point. Experiments on datasets from UCI ML Repository show that, very often, we can find a suitable Pareto optimal point and derive a decision tree with small number of nodes at the expense of small increment in number of misclassification. Based on the created approach we have proposed a multi-pruning procedure which constructs decision trees that, as classifiers, often outperform decision trees constructed by CART. © 2015 IEEE.

  13. Greedy heuristics for minimization of number of terminal nodes in decision trees

    KAUST Repository

    Hussain, Shahid

    2014-10-01

    This paper describes, in detail, several greedy heuristics for construction of decision trees. We study the number of terminal nodes of decision trees, which is closely related with the cardinality of the set of rules corresponding to the tree. We compare these heuristics empirically for two different types of datasets (datasets acquired from UCI ML Repository and randomly generated data) as well as compare with the optimal results obtained using dynamic programming method.

  14. Greedy heuristics for minimization of number of terminal nodes in decision trees

    KAUST Repository

    Hussain, Shahid

    2014-01-01

    This paper describes, in detail, several greedy heuristics for construction of decision trees. We study the number of terminal nodes of decision trees, which is closely related with the cardinality of the set of rules corresponding to the tree. We compare these heuristics empirically for two different types of datasets (datasets acquired from UCI ML Repository and randomly generated data) as well as compare with the optimal results obtained using dynamic programming method.

  15. A new fuzzy MCDA framework for make-or-buy decisions: A case study of aerospace industry

    Directory of Open Access Journals (Sweden)

    Mohsen Cheshmberah

    2011-07-01

    Full Text Available One of the primary managerial decisions for manufacturing units is to find out which activity must be outsourced. A good outsourcing decision is normally involved with different criteria such as opportunity costs, cost saving, etc. In this paper, we present a multi criteria decision-making method to find a suitable solution for outsourcing activities called preference ranking organization method for enrichment evaluations (PROMETHEE. The proposed model of this paper uses fuzzy numbers to determine the relative importance of different criteria and it is implemented for a real-world case study of aerospace industry.

  16. A decision making method based on interval type-2 fuzzy sets: An approach for ambulance location preference

    Directory of Open Access Journals (Sweden)

    Lazim Abdullah

    2018-01-01

    Full Text Available Selecting the best solution to deploy an ambulance in a strategic location is of the important variables that need to be accounted for improving the emergency medical services. The selection requires both quantitative and qualitative evaluation. Fuzzy set based approach is one of the well-known theories that help decision makers to handle fuzziness, uncertainty in decision making and vagueness of information. This paper proposes a new decision making method of Interval Type-2 Fuzzy Simple Additive Weighting (IT2 FSAW as to deal with uncertainty and vagueness. The new IT2 FSAW is applied to establish a preference in ambulance location. The decision making framework defines four criteria and five alternatives of ambulance location preference. Four experts attached to a Malaysian government hospital and a university medical center were interviewed to provide linguistic evaluation prior to analyzing with the new IT2 FSAW. Implementation of the proposed method in the case of ambulance location preference suggests that the ‘road network’ is the best alternative for ambulance location. The results indicate that the proposed method offers a consensus solution for handling the vague and qualitative criteria of ambulance location preference.

  17. Prioritize Improvement Opportunities Identified In Self-Assessment Using Multi-Criteria Fuzzy Group Decision

    Directory of Open Access Journals (Sweden)

    Dr.Ghassem Faraj Pour

    2015-05-01

    Full Text Available ABSTRACT Efforts to improve the quality are one of the prerequisites for the success of individual companies and for the competitiveness of all whole companies. In the field of improvement and excellence business excellence models answer to the question that what the better organization is what goals and concepts they follow and according to what standards they behave. The EFQM excellence model can be transition from multiplicity to unity of different existing models. The most important approaches of these models are self-assessment and identifying improvement areas in an organization. On the other side organizations which are at lower level of total quality management will encounter so many areas to improve when using this model and implementing of self-improvement. Choosing the most important key problems are always the main challenges and because of resource constraints and strategic goals organizations have to prioritize identified improvement opportunities. This paper introduces a model for prioritizing and choosing the most significant improvement opportunities using the organization Business Excellence team members and because the analysis and decision making atmosphere for excellence team members is not generally complete with accurate information it seems using of fuzzy decision can be very helpful.

  18. Fuzzy Simple Additive Weighting Method in the Decision Making of Human Resource Recruitment

    Directory of Open Access Journals (Sweden)

    Budi Prasetiyo

    2016-12-01

    Full Text Available The Company is one of the jobs that was founded to reduce unemployment. The progress of a company is determined by the human resources that exist within the company. So, the selection of workers will join the company need to be selected first. The hardest thing in making a selection factor is the effort to eliminate the subjectivity of the personnel manager so that every choice made is objective based on the criteria expected by the company. To help determine who is accepted as an employee in the company, we need a method that can provide a valid decision. Therefore, we use Fuzzy Multiple Attribute Decision Making with Simple Additive Weighting method (SAW to decide to make in human resource recruitment. This method was chosen because it can provide the best alternative from several alternatives. In this case, the alternative is that the applicants or candidates. This research was conducted by finding the weight values for each attribute. Then do the ranking process that determines the optimal alternative to the best applicants who qualify as employees of the company. Based on calculations by the SAW obtained the two highest ranking results are A5 (alternative 5 and A1 (alternative 1, to obtain two candidates received.

  19. Multi-test decision tree and its application to microarray data classification.

    Science.gov (United States)

    Czajkowski, Marcin; Grześ, Marek; Kretowski, Marek

    2014-05-01

    The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on 14 datasets by an average 6%. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts. Copyright © 2014 Elsevier B.V. All rights reserved.

  20. Decision Rules, Trees and Tests for Tables with Many-valued Decisions–comparative Study

    KAUST Repository

    Azad, Mohammad

    2013-10-04

    In this paper, we present three approaches for construction of decision rules for decision tables with many-valued decisions. We construct decision rules directly for rows of decision table, based on paths in decision tree, and based on attributes contained in a test (super-reduct). Experimental results for the data sets taken from UCI Machine Learning Repository, contain comparison of the maximum and the average length of rules for the mentioned approaches.

  1. Decision Rules, Trees and Tests for Tables with Many-valued Decisions–comparative Study

    KAUST Repository

    Azad, Mohammad; Zielosko, Beata; Moshkov, Mikhail; Chikalov, Igor

    2013-01-01

    In this paper, we present three approaches for construction of decision rules for decision tables with many-valued decisions. We construct decision rules directly for rows of decision table, based on paths in decision tree, and based on attributes contained in a test (super-reduct). Experimental results for the data sets taken from UCI Machine Learning Repository, contain comparison of the maximum and the average length of rules for the mentioned approaches.

  2. Hesitant fuzzy sets theory

    CERN Document Server

    Xu, Zeshui

    2014-01-01

    This book provides the readers with a thorough and systematic introduction to hesitant fuzzy theory. It presents the most recent research results and advanced methods in the field. These includes: hesitant fuzzy aggregation techniques, hesitant fuzzy preference relations, hesitant fuzzy measures, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods. Since its introduction by Torra and Narukawa in 2009, hesitant fuzzy sets have become more and more popular and have been used for a wide range of applications, from decision-making problems to cluster analysis, from medical diagnosis to personnel appraisal and information retrieval. This book offers a comprehensive report on the state-of-the-art in hesitant fuzzy sets theory and applications, aiming at becoming a reference guide for both researchers and practitioners in the area of fuzzy mathematics and other applied research fields (e.g. operations research, information science, management science and engineering) chara...

  3. Fuzzy logic in management

    CERN Document Server

    Carlsson, Christer; Fullér, Robert

    2004-01-01

    Fuzzy Logic in Management demonstrates that difficult problems and changes in the management environment can be more easily handled by bringing fuzzy logic into the practice of management. This explicit theme is developed through the book as follows: Chapter 1, "Management and Intelligent Support Technologies", is a short survey of management leadership and what can be gained from support technologies. Chapter 2, "Fuzzy Sets and Fuzzy Logic", provides a short introduction to fuzzy sets, fuzzy relations, the extension principle, fuzzy implications and linguistic variables. Chapter 3, "Group Decision Support Systems", deals with group decision making, and discusses methods for supporting the consensus reaching processes. Chapter 4, "Fuzzy Real Options for Strategic Planning", summarizes research where the fuzzy real options theory was implemented as a series of models. These models were thoroughly tested on a number of real life investments, and validated in 2001. Chapter 5, "Soft Computing Methods for Reducing...

  4. The decision tree classifier - Design and potential. [for Landsat-1 data

    Science.gov (United States)

    Hauska, H.; Swain, P. H.

    1975-01-01

    A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

  5. Logistics Service Provider Selection through an Integrated Fuzzy Multicriteria Decision Making Approach

    OpenAIRE

    Gülşen Akman; Kasım Baynal

    2014-01-01

    Nowadays, the demand of third-party logistics provider becomes an increasingly important issue for companies to improve their customer service and to decrease logistics costs. This paper presents an integrated fuzzy approach for the evaluation and selection of 3rd party logistics service providers. This method consists of two techniques: (1) use fuzzy analytic hierarchy process to identify weights of evaluation criteria; (2) apply fuzzy technique for order preference by similarity to ideal so...

  6. Intuitionistic Trapezoidal Fuzzy Group Decision-Making Based on Prospect Choquet Integral Operator and Grey Projection Pursuit Dynamic Cluster

    Directory of Open Access Journals (Sweden)

    Jiahang Yuan

    2017-01-01

    Full Text Available In consideration of the interaction among attributes and the influence of decision makers’ risk attitude, this paper proposes an intuitionistic trapezoidal fuzzy aggregation operator based on Choquet integral and prospect theory. With respect to a multiattribute group decision-making problem, the prospect value functions of intuitionistic trapezoidal fuzzy numbers are aggregated by the proposed operator; then a grey relation-projection pursuit dynamic cluster method is developed to obtain the ranking of alternatives; the firefly algorithm is used to optimize the objective function of projection for obtaining the best projection direction of grey correlation projection values, and the grey correlation projection values are evaluated, which are applied to classify, rank, and prefer the alternatives. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible.

  7. Use of fault and decision tree analyses to protect against industrial sabotage

    International Nuclear Information System (INIS)

    Fullwood, R.R.; Erdmann, R.C.

    1975-01-01

    Fault tree and decision tree analyses provide systematic bases for evaluation of safety systems and procedures. Heuristically, this paper shows applications of these methods for industrial sabotage analysis at a reprocessing plant. Fault trees constructed by ''leak path'' analysis for completeness through path inventory. The escape fault tree is readily developed by this method and using the reciprocal character of the trees, the attack fault tree is constructed. After construction, the events on the fault tree are corrected for their nonreciprocal character. The fault trees are algebraically solved and the protection that is afforded is ranked by the number of barriers that must be penetrated. No attempt is made to assess the barrier penetration probabilities or penetration time duration. Event trees are useful for dynamic plant protection analysis through their time-sequencing character. To illustrate their usefulness, a simple attack scenario is devised and event-tree analyzed. Two saboteur success paths and 21 failure paths are found. This example clearly shows the event tree usefulness for concisely presenting the time sequencing of key decision points. However, event trees have the disadvantage of being scenario dependent, therefore requiring a separate event tree for each scenario

  8. Learning from examples - Generation and evaluation of decision trees for software resource analysis

    Science.gov (United States)

    Selby, Richard W.; Porter, Adam A.

    1988-01-01

    A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

  9. ArborZ: PHOTOMETRIC REDSHIFTS USING BOOSTED DECISION TREES

    International Nuclear Information System (INIS)

    Gerdes, David W.; Sypniewski, Adam J.; McKay, Timothy A.; Hao, Jiangang; Weis, Matthew R.; Wechsler, Risa H.; Busha, Michael T.

    2010-01-01

    Precision photometric redshifts will be essential for extracting cosmological parameters from the next generation of wide-area imaging surveys. In this paper, we introduce a photometric redshift algorithm, ArborZ, based on the machine-learning technique of boosted decision trees. We study the algorithm using galaxies from the Sloan Digital Sky Survey (SDSS) and from mock catalogs intended to simulate both the SDSS and the upcoming Dark Energy Survey. We show that it improves upon the performance of existing algorithms. Moreover, the method naturally leads to the reconstruction of a full probability density function (PDF) for the photometric redshift of each galaxy, not merely a single 'best estimate' and error, and also provides a photo-z quality figure of merit for each galaxy that can be used to reject outliers. We show that the stacked PDFs yield a more accurate reconstruction of the redshift distribution N(z). We discuss limitations of the current algorithm and ideas for future work.

  10. Prediction of adverse drug reactions using decision tree modeling.

    Science.gov (United States)

    Hammann, F; Gutmann, H; Vogt, N; Helma, C; Drewe, J

    2010-07-01

    Drug safety is of great importance to public health. The detrimental effects of drugs not only limit their application but also cause suffering in individual patients and evoke distrust of pharmacotherapy. For the purpose of identifying drugs that could be suspected of causing adverse reactions, we present a structure-activity relationship analysis of adverse drug reactions (ADRs) in the central nervous system (CNS), liver, and kidney, and also of allergic reactions, for a broad variety of drugs (n = 507) from the Swiss drug registry. Using decision tree induction, a machine learning method, we determined the chemical, physical, and structural properties of compounds that predispose them to causing ADRs. The models had high predictive accuracies (78.9-90.2%) for allergic, renal, CNS, and hepatic ADRs. We show the feasibility of predicting complex end-organ effects using simple models that involve no expensive computations and that can be used (i) in the selection of the compound during the drug discovery stage, (ii) to understand how drugs interact with the target organ systems, and (iii) for generating alerts in postmarketing drug surveillance and pharmacovigilance.

  11. Discovering Patterns in Brain Signals Using Decision Trees

    Directory of Open Access Journals (Sweden)

    Narusci S. Bastos

    2016-01-01

    Full Text Available Even with emerging technologies, such as Brain-Computer Interfaces (BCI systems, understanding how our brains work is a very difficult challenge. So we propose to use a data mining technique to help us in this task. As a case of study, we analyzed the brain’s behaviour of blind people and sighted people in a spatial activity. There is a common belief that blind people compensate their lack of vision using the other senses. If an object is given to sighted people and we asked them to identify this object, probably the sense of vision will be the most determinant one. If the same experiment was repeated with blind people, they will have to use other senses to identify the object. In this work, we propose a methodology that uses decision trees (DT to investigate the difference of how the brains of blind people and people with vision react against a spatial problem. We choose the DT algorithm because it can discover patterns in the brain signal, and its presentation is human interpretable. Our results show that using DT to analyze brain signals can help us to understand the brain’s behaviour.

  12. Application of alternating decision trees in selecting sparse linear solvers

    KAUST Repository

    Bhowmick, Sanjukta; Eijkhout, Victor; Freund, Yoav; Fuentes, Erika; Keyes, David E.

    2010-01-01

    The solution of sparse linear systems, a fundamental and resource-intensive task in scientific computing, can be approached through multiple algorithms. Using an algorithm well adapted to characteristics of the task can significantly enhance the performance, such as reducing the time required for the operation, without compromising the quality of the result. However, the best solution method can vary even across linear systems generated in course of the same PDE-based simulation, thereby making solver selection a very challenging problem. In this paper, we use a machine learning technique, Alternating Decision Trees (ADT), to select efficient solvers based on the properties of sparse linear systems and runtime-dependent features, such as the stages of simulation. We demonstrate the effectiveness of this method through empirical results over linear systems drawn from computational fluid dynamics and magnetohydrodynamics applications. The results also demonstrate that using ADT can resolve the problem of over-fitting, which occurs when limited amount of data is available. © 2010 Springer Science+Business Media LLC.

  13. Efficient OCR using simple features and decision trees with backtracking

    International Nuclear Information System (INIS)

    Abuhaiba, Ibrahim S.I.

    2006-01-01

    In this paper, it is shown that it is adequate to use simple and easy-to-compute figures such as those we call sliced horizontal and vertical projections to solve the OCR problem for machine-printed documents. Recognition is achieved using a decision tree supported with backtracking, smoothing, row and column cropping, and other additions to increase the success rate. Symbols from Times New Roman type face are used to train our system. Activating backtracking, smoothing and cropping achieved more than 98% successes rate for a recognition time below 30ms per character. The recognition algorithm was exposed to a hard test by polluting the original dataset with additional artificial noise and could maintain a high successes rate and low error rate for highly polluted images, which is a result of backtracking, and smoothing and row and column cropping. Results indicate that we can depend on simple features and hints to reliably recognize characters. The error rate can be decreased by increasing the size of training dataset. The recognition time can be reduced by using some programming optimization techniques and more powerful computers. (author)

  14. Decision trees and decision committee applied to star/galaxy separation problem

    Science.gov (United States)

    Vasconcellos, Eduardo Charles

    Vasconcellos et al [1] study the efficiency of 13 diferente decision tree algorithms applied to photometric data in the Sloan Digital Sky Digital Survey Data Release Seven (SDSS-DR7) to perform star/galaxy separation. Each algorithm is defined by a set fo parameters which, when varied, produce diferente final classifications trees. In that work we extensively explore the parameter space of each algorithm, using the set of 884,126 SDSS objects with spectroscopic data as the training set. We find that Functional Tree algorithm (FT) yields the best results by the mean completeness function (galaxy true positive rate) in two magnitude intervals:14=19 (82.1%). We compare FT classification to the SDSS parametric, 2DPHOT and Ball et al (2006) classifications. At the faintest magnitudes (r > 19), our classifier is the only one that maintains high completeness (>80%) while simultaneously achieving low contamination ( 2.5%). We also examine the SDSS parametric classifier (psfMag - modelMag) to see if the dividing line between stars and galaxies can be adjusted to improve the classifier. We find that currently stars in close pairs are often misclassified as galaxies, and suggest a new cut to improve the classifier. Finally, we apply our FT classifier to separate stars from galaxies in the full set of 69,545,326 SDSS photometric objects in the magnitude range 14 train six FT classifiers with random selected objects from the same 884,126 SDSS-DR7 objects with spectroscopic data that we use before. Both, the decision commitee and our previous single FT classifier will be applied to the new ojects from SDSS data releses eight, nine and ten. Finally we will compare peformances of both methods in this new data set. [1] Vasconcellos, E. C.; de Carvalho, R. R.; Gal, R. R.; LaBarbera, F. L.; Capelato, H. V.; Fraga Campos Velho, H.; Trevisan, M.; Ruiz, R. S. R.. Decision Tree Classifiers for Star/Galaxy Separation. The Astronomical Journal, Volume 141, Issue 6, 2011.

  15. Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

    Science.gov (United States)

    Hor, Soheil; Moradi, Mehdi

    2016-12-01

    Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

  16. Reconciliation as a tool for decision making within decision tree related to insolvency problems

    Directory of Open Access Journals (Sweden)

    Tomáš Poláček

    2016-05-01

    Full Text Available Purpose of the article: The paper draws on the results of previous studies recoverability of creditor’s claims, where it was research from debtor’s point of view and his/her debts on the Czech Republic financial market. The company, which fell into a bankruptcy hearing, has several legislatively supported options how to deal with this situation and repay creditors money. Each of the options has been specified as a variant of a decisionmaking tree. This paper is focused on third option of evaluation – The reconciliation. The heuristic generates all missing information items. The result is then focused on the comparison and evaluation of the best ways to repay the debt, also including solution for the future continuation of the company currently in liquidation and quantification of percentage refund of creditors claim. A realistic case study is presented in full details. Further introduction of decision making with uncerteinties in insolvency proceedings. Methodology/methods: Solving within decision tree with partially ignorance of probability using reconciliation. Scientific aim: Comparison and evaluation of the best ways to repay the debt, also including solution for the future continuation of the company currently in liquidation and quantification of percentage refund of creditors claim. Findings: Predictions of future actions in dealing with insolvency act and bankruptcy hearing, quicker and more effective agreeing on compromises among all creditors and debtor. Conclusions: Finding a best way and solution of repayment and avoiding of termination for both of interested parties (creditor and debtor.

  17. Iron Supplementation and Altitude: Decision Making Using a Regression Tree

    Directory of Open Access Journals (Sweden)

    Laura A. Garvican-Lewis, Andrew D. Govus, Peter Peeling, Chris R. Abbiss, Christopher J. Gore

    2016-03-01

    Full Text Available Altitude exposure increases the body’s need for iron (Gassmann and Muckenthaler, 2015, primarily to support accelerated erythropoiesis, yet clear supplementation guidelines do not exist. Athletes are typically recommended to ingest a daily oral iron supplement to facilitate altitude adaptations, and to help maintain iron balance. However, there is some debate as to whether athletes with otherwise healthy iron stores should be supplemented, due in part to concerns of iron overload. Excess iron in vital organs is associated with an increased risk of a number of conditions including cancer, liver disease and heart failure. Therefore clear guidelines are warranted and athletes should be discouraged from ‘self-prescribing” supplementation without medical advice. In the absence of prospective-controlled studies, decision tree analysis can be used to describe a data set, with the resultant regression tree serving as guide for clinical decision making. Here, we present a regression tree in the context of iron supplementation during altitude exposure, to examine the association between pre-altitude ferritin (Ferritin-Pre and the haemoglobin mass (Hbmass response, based on daily iron supplement dose. De-identified ferritin and Hbmass data from 178 athletes engaged in altitude training were extracted from the Australian Institute of Sport (AIS database. Altitude exposure was predominantly achieved via normobaric Live high: Train low (n = 147 at a simulated altitude of 3000 m for 2 to 4 weeks. The remaining athletes engaged in natural altitude training at venues ranging from 1350 to 2800 m for 3-4 weeks. Thus, the “hypoxic dose” ranged from ~890 km.h to ~1400 km.h. Ethical approval was granted by the AIS Human Ethics Committee, and athletes provided written informed consent. An in depth description and traditional analysis of the complete data set is presented elsewhere (Govus et al., 2015. Iron supplementation was prescribed by a sports physician

  18. A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging.

    Science.gov (United States)

    Nair, Shalini Rajandran; Tan, Li Kuo; Mohd Ramli, Norlisah; Lim, Shen Yang; Rahmat, Kartini; Mohd Nor, Hazman

    2013-06-01

    To develop a decision tree based on standard magnetic resonance imaging (MRI) and diffusion tensor imaging to differentiate multiple system atrophy (MSA) from Parkinson's disease (PD). 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3. Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified. Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD. • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.

  19. A greedy algorithm for construction of decision trees for tables with many-valued decisions - A comparative study

    KAUST Repository

    Azad, Mohammad

    2013-11-25

    In the paper, we study a greedy algorithm for construction of decision trees. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. Experimental results for data sets from UCI Machine Learning Repository and randomly generated tables are presented. We make a comparative study of the depth and average depth of the constructed decision trees for proposed approach and approach based on generalized decision. The obtained results show that the proposed approach can be useful from the point of view of knowledge representation and algorithm construction.

  20. A greedy algorithm for construction of decision trees for tables with many-valued decisions - A comparative study

    KAUST Repository

    Azad, Mohammad; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

    2013-01-01

    In the paper, we study a greedy algorithm for construction of decision trees. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. Experimental results for data sets from UCI Machine Learning Repository and randomly generated tables are presented. We make a comparative study of the depth and average depth of the constructed decision trees for proposed approach and approach based on generalized decision. The obtained results show that the proposed approach can be useful from the point of view of knowledge representation and algorithm construction.

  1. Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine

    Science.gov (United States)

    Schwabacher, Mark A.; Aguilar, Robert; Figueroa, Fernando F.

    2009-01-01

    The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

  2. The Sustainable Island Development Evaluation Model and Its Application Based on the Nonstructural Decision Fuzzy Set

    Directory of Open Access Journals (Sweden)

    Quanming Wang

    2013-01-01

    Full Text Available Due to the complexity and diversity of the issue of sustainable island development, no widely accepted and applicable evaluation system model regarding the issue currently exists. In this paper, we discuss and establish the sustainable development indicator system and the model approach from the perspective of resources, the island environment, the island development status, the island social development, and the island intelligence development. We reference the sustainable development theory and the sustainable development indicator system method concerning land region, combine the character of the sustainable island development, analyze and evaluate the extent of the sustainable island development, orient development, and identify the key and limited factors of sustainable island development capability. This research adopts the entropy method and the nonstructural decision fuzzy set theory model to determine the weight of the evaluating indicators. Changhai County was selected as the subject of the research, which consisted of a quantitative study of its sustainable development status from 2001 to 2008 to identify the key factors influencing its sustainability development, existing problems, and limited factors and to provide basic technical support for ocean development planning and economic development planning.

  3. A New Group Decision Model Based on Grey-Intuitionistic Fuzzy-ELECTRE and VIKOR for Contractor Assessment Problem

    Directory of Open Access Journals (Sweden)

    Hassan Hashemi

    2018-05-01

    Full Text Available This study introduces a new decision model with multi-criteria analysis by a group of decision makers (DMs with intuitionistic fuzzy sets (IFSs. The presented model depends on a new integration of IFSs theory, ELECTRE and VIKOR along with grey relational analysis (GRA. To portray uncertain real-life situations and take account of complex decision problem, multi-criteria group decision-making (MCGDM model by totally unknown importance are introduced with IF-setting. Hence, a weighting method depended on Entropy and IFSs, is developed to present the weights of DMs and evaluation factors. A new ranking approach is provided for prioritizing the alternatives. To indicate the applicability of the presented new decision model, an industrial application for assessing contractors in the construction industry is given and discussed from the recent literature.

  4. Detailed Sponge City Planning Based on Hierarchical Fuzzy Decision-Making: A Case Study on Yangchen Lake

    Directory of Open Access Journals (Sweden)

    Junyu Zhang

    2017-11-01

    Full Text Available We proposed a Hierarchical Fuzzy Inference System (HFIS framework to offer better decision supports with fewer user-defined data (uncertainty. The framework consists two parts: a fuzzified Geographic Information System (GIS and a HFIS system. The former provides comprehensive information on the criterion unit and the latter helps in making more robust decisions. The HFIS and the traditional Multi-Criteria Decision Making (MCDM method were applied to a case study and compared. The fuzzified GIS maps maintained a majority of the dominant characteristics of the criterion unit but also revealed some non-significant information according to the surrounding environment. The urban planning map generated by the two methods shares similar strategy choices (6% difference, while the spatial distribution of strategies shares 69.7% in common. The HFIS required fewer subjective decisions than the MCDM (34 user-defined decision rules vs. 141 manual evaluations.

  5. A multiple criteria decision making for raking alternatives using preference relation matrix based on intuitionistic fuzzy sets

    Directory of Open Access Journals (Sweden)

    Mehdi Bahramloo

    2013-10-01

    Full Text Available Ranking various alternatives has been under investigation and there are literally various methods and techniques for making a decision based on various criteria. One of the primary concerns on ranking methodologies such as analytical hierarchy process (AHP is that decision makers cannot express his/her feeling in crisp form. Therefore, we need to use linguistic terms to receive the relative weights for comparing various alternatives. In this paper, we discuss ranking different alternatives based on the implementation of preference relation matrix based on intuitionistic fuzzy sets.

  6. Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

    Science.gov (United States)

    Cantu-Paz, Erick [Oakland, CA; Kamath, Chandrika [Tracy, CA

    2006-06-13

    A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

  7. Improved Frame Mode Selection for AMR-WB+ Based on Decision Tree

    Science.gov (United States)

    Kim, Jong Kyu; Kim, Nam Soo

    In this letter, we propose a coding mode selection method for the AMR-WB+ audio coder based on a decision tree. In order to reduce computation while maintaining good performance, decision tree classifier is adopted with the closed loop mode selection results as the target classification labels. The size of the decision tree is controlled by pruning, so the proposed method does not increase the memory requirement significantly. Through an evaluation test on a database covering both speech and music materials, the proposed method is found to achieve a much better mode selection accuracy compared with the open loop mode selection module in the AMR-WB+.

  8. Discovering Decision Knowledge from Web Log Portfolio for Managing Classroom Processes by Applying Decision Tree and Data Cube Technology.

    Science.gov (United States)

    Chen, Gwo-Dong; Liu, Chen-Chung; Ou, Kuo-Liang; Liu, Baw-Jhiune

    2000-01-01

    Discusses the use of Web logs to record student behavior that can assist teachers in assessing performance and making curriculum decisions for distance learning students who are using Web-based learning systems. Adopts decision tree and data cube information processing methodologies for developing more effective pedagogical strategies. (LRW)

  9. Fuzziness, democracy, control and collective decision-choice system a theory on political economy of rent-seeking and profit-harvesting

    CERN Document Server

    Dompere, Kofi Kissi

    2014-01-01

    This volume presents an analysis of the problems and solutions of the market mockery of the democratic collective decision-choice system with imperfect information structure composed of defective and deceptive structures using methods of fuzzy rationality. The book is devoted to the political economy of rent-seeking, rent-protection and rent-harvesting to enhance profits under democratic collective decision-choice systems. The toolbox used in the monograph consists of methods of fuzzy decision, approximate reasoning, negotiation games and fuzzy mathematics. The monograph further discusses the rent-seeking phenomenon in the Schumpeterian and Marxian political economies where the rent-seeking activities transform the qualitative character of the general capitalism into oligarchic socialism and making the democratic collective decision-choice system as an ideology rather than social calculus for resolving conflicts in preferences in the collective decision-choice space without violence.    

  10. Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

    Science.gov (United States)

    Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

    2018-01-19

    OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

  11. Fuzzy algorithmic and knowledge-based decision support in nuclear engineering

    International Nuclear Information System (INIS)

    Zimmermann, H. J.

    1996-01-01

    Fuzzy Set Theory was originally conceived as a means to model non- stochastic uncertainty. In the meantime it has matured to Fuzzy Technology and - together with Neural Nets and Genetic Algorithms - to Computational Intelligence. The goals have expanded considerably. In addition to uncertainty modeling, relaxation, compactification and meaning preserving reasoning have become major objectives. Nuclear engineering is one of the areas with a large potential for applications of Fuzzy Technologies, in which, however, the development is still at the beginning. This paper tries to survey applications and point to some potential applications which have not yet been realized

  12. Application of Fuzzy theory with neutral network and cognitive map on decision making

    International Nuclear Information System (INIS)

    Hla Aung; Tin Maung

    2001-01-01

    The format reasoning involves establishment of causal relationships among concepts. These are commonly represented by cognitive maps. However, the concepts and their relationships could be fuzzy. In this paper we review some properties of fuzzy cognitive maps. This paper shows that one of the solutions is to introduce the idea of disconcepts along with concepts to arrive at reasonings that are intuitively satisfying. A neutral network architecture based on associative memory and a framework for fuzzy cognitive maps based knowledge processing tool has also been proposed. The proposed method is tested on a cognitive map of a publishing company. (author)

  13. Using Decision Trees to Detect and Isolate Leaks in the J-2X

    Data.gov (United States)

    National Aeronautics and Space Administration — Full title: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine Mark Schwabacher, NASA Ames Research Center Robert Aguilar, Pratt...

  14. Comparison of the use of binary decision trees and neural networks in top-quark detection

    International Nuclear Information System (INIS)

    Bowser-Chao, D.; Dzialo, D.L.

    1993-01-01

    The use of neural networks for signal versus background discrimination in high-energy physics experiments has been investigated and has compared favorably with the efficiency of traditional kinematic cuts. Recent work in top-quark identification produced a neural network that, for a given top-quark mass, yielded a higher signal-to-background ratio in Monte Carlo simulation than a corresponding set of conventional cuts. In this article we discuss another pattern-recognition algorithm, the binary decision tree. We apply a binary decision tree to top-quark identification at the Fermilab Tevatron and find it to be comparable in performance to the neural network. Furthermore, reservations about the ''black box'' nature of neural network discriminators do not appy to binary decision trees; a binary decision tree may be reduced to a set of kinematic cuts subject to conventional error analysis

  15. Using decision trees and their ensembles for analysis of NIR spectroscopic data

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.

    and interpretation of the models. In this presentation, we are going to discuss an applicability of decision trees based methods (including gradient boosting) for solving classification and regression tasks with NIR spectra as predictors. We will cover such aspects as evaluation, optimization and validation......Advanced machine learning methods, like convolutional neural networks and decision trees, became extremely popular in the last decade. This, first of all, is directly related to the current boom in Big data analysis, where traditional statistical methods are not efficient. According to the kaggle.......com — the most popular online resource for Big data problems and solutions — methods based on decision trees and their ensembles are most widely used for solving the problems. It can be noted that the decision trees and convolutional neural networks are not very popular in Chemometrics. One of the reasons...

  16. Detecting Structural Metadata with Decision Trees and Transformation-Based Learning

    National Research Council Canada - National Science Library

    Kim, Joungbum; Schwarm, Sarah E; Ostendorf, Mari

    2004-01-01

    .... Specifically, combinations of decision trees and language models are used to predict sentence ends and interruption points and given these events transformation based learning is used to detect edit...

  17. Statistical Sensitive Data Protection and Inference Prevention with Decision Tree Methods

    National Research Council Canada - National Science Library

    Chang, LiWu

    2003-01-01

    .... We consider inference as correct classification and approach it with decision tree methods. As in our previous work, sensitive data are viewed as classes of those test data and non-sensitive data are the rest attribute values...

  18. Application of preprocessing filtering on Decision Tree C4.5 and rough set theory

    Science.gov (United States)

    Chan, Joseph C. C.; Lin, Tsau Y.

    2001-03-01

    This paper compares two artificial intelligence methods: the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the feature (attribute) transformations, which allows users to input formulas and create new attributes. Also, the application produces three varieties of data set with delaying, averaging, and summation. The results prove the improvement of pre-processing by applying feature (attribute) transformations on Decision Tree C4.5. Moreover, the comparison between Decision Tree C4.5 and Rough Set Theory is based on the clarity, automation, accuracy, dimensionality, raw data, and speed, which is supported by the rules sets generated by both algorithms on three different sets of data.

  19. Totally Optimal Decision Trees for Monotone Boolean Functions with at Most Five Variables

    KAUST Repository

    Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2013-01-01

    In this paper, we present the empirical results for relationships between time (depth) and space (number of nodes) complexity of decision trees computing monotone Boolean functions, with at most five variables. We use Dagger (a tool for optimization

  20. A fuzzy approach to a multiple criteria and Geographical Information System for decision support on suitable locations for biogas plants

    DEFF Research Database (Denmark)

    Franco, Camilo; Bojesen, Mikkel; Hougaard, Jens Leth

    2015-01-01

    The purpose of this paper is to model the multi-criteria decision problem of identifying the most suitable facility locations for biogas plants under an integrated decision support methodology. Here the Geographical Information System (GIS) is used for measuring the attributes of the alternatives...... according to a given set of criteria. Measurements are taken in interval form, expressing the natural imprecision of common data, and the Fuzzy Weighted Overlap Dominance (FWOD) procedure is applied for aggregating and exploiting this kind of data, obtaining suitability degrees for every alternative...... suitable sites for building biogas plants. We show that the FWOD relevance-ranking procedure can also be successfully applied over the outcomes of different decision makers, in case a unique social solution is required to exist. The proposed methodology can be used under an integrated decision support...

  1. Construction and application of hierarchical decision tree for classification of ultrasonographic prostate images

    NARCIS (Netherlands)

    Giesen, R. J.; Huynen, A. L.; Aarnink, R. G.; de la Rosette, J. J.; Debruyne, F. M.; Wijkstra, H.

    1996-01-01

    A non-parametric algorithm is described for the construction of a binary decision tree classifier. This tree is used to correlate textural features, computed from ultrasonographic prostate images, with the histopathology of the imaged tissue. The algorithm consists of two parts; growing and pruning.

  2. Learning decision trees with flexible constraints and objectives using integer optimization

    NARCIS (Netherlands)

    Verwer, S.; Zhang, Y.

    2017-01-01

    We encode the problem of learning the optimal decision tree of a given depth as an integer optimization problem. We show experimentally that our method (DTIP) can be used to learn good trees up to depth 5 from data sets of size up to 1000. In addition to being efficient, our new formulation allows

  3. Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic.

    Science.gov (United States)

    Dehghani Soufi, Mahsa; Samad-Soltani, Taha; Shams Vahdati, Samad; Rezaei-Hachesu, Peyman

    2018-06-01

    Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to determine the triage level. A combination of the Rule-Based Reasoning (RBR) and Fuzzy Logic Classifier (FLC) approaches were used to predict the triage level of patients according to the triage specialist's opinions and Emergency Severity Index (ESI) guidelines. RBR was applied for modeling the first to fourth decision points of the ESI algorithm. The data relating to vital signs were used as input variables and modeled using fuzzy logic. Narrative knowledge was converted to If-Then rules using XML. The extracted rules were then used to create the rule-based engine and predict the triage levels. Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of the documentation also improved from 76.72% to 98.5%. Designed system was effective in determining the triage level of patients and it proved helpful for nurses as they made decisions, generated nursing diagnoses based on triage guidelines. The hybrid approach can reduce triage misdiagnosis in a highly accurate manner and improve the triage outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.

  4. A fuzzy multi-criteria decision-making model for CCHP systems driven by different energy sources

    International Nuclear Information System (INIS)

    Jing Youyin; Bai He; Wang Jiangjiang

    2012-01-01

    Because of its energy-saving and pollutant emission reduction potentials, combined cooling, heating and power (CCHP) system has been widely used in different kinds of buildings to solve building-related energetic problems and environmental issues. As various kinds of clean energy and renewable energy have been focused and applied to CCHP systems, it is urgent to find a practical decision making methodology for CCHP systems driven by different energy sources. In this paper, an evaluation model which integrates fuzzy theory with multi-criteria decision making process is proposed to assess the comprehensive benefits of CCHP systems from technology, economic, society and environment criterions. Grey relation analysis and combination weighting method are also employed to compare the integrated performances of CCHP systems driven by natural gas, fuel cell, biomass energy and combined gas-steam cycle respectively with a separation production system. Finally, a baseline residential building in Beijing, China is selected as a case to obtain the optimal CCHP system alternative. The results indicate that gas–steam combined cycle CCHP system is the optimum scheme among the five options. - Graphical abstract: A fuzzy multi-criteria decision-making model combined with combination weighting method and grey system theory is presented in this paper, which can be used to evaluate CCHP systems driven by different energy sources from technology, economic, environment and society criteria. Highlights: ► The integrated benefits of CCHP systems driven by different energy sources are evaluated. ► A fuzzy multi-criteria model combined with combination weighting method is proposed. ► Environment evaluation criteria play an important role in the decision-making process. ► CCHP system driven by gas–steam combined cycle is the optimal alternative.

  5. Assessment of health-care waste disposal methods using a VIKOR-based fuzzy multi-criteria decision making method

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Hu-Chen [School of Management, Hefei University of Technology, Hefei 230009 (China); Department of Industrial Engineering and Management, Tokyo Institute of Technology, Tokyo 152-8552 (Japan); Wu, Jing [Department of Public Management, Tongji University, Shanghai 200092 (China); Li, Ping, E-mail: yiwuchulp@126.com [Shanghai Pudong New Area Zhoupu Hospital, No. 135 Guanyue Road, Shanghai 201318 (China); East Hospital Affiliated to Tongji University, No. 150 Jimo Road, Shanghai 200120 (China)

    2013-12-15

    Highlights: • Propose a VIKOR-based fuzzy MCDM technique for evaluating HCW disposal methods. • Linguistic variables are used to assess the ratings and weights for the criteria. • The OWA operator is utilized to aggregate individual opinions of decision makers. • A case study is given to illustrate the procedure of the proposed framework. - Abstract: Nowadays selection of the appropriate treatment method in health-care waste (HCW) management has become a challenge task for the municipal authorities especially in developing countries. Assessment of HCW disposal alternatives can be regarded as a complicated multi-criteria decision making (MCDM) problem which requires consideration of multiple alternative solutions and conflicting tangible and intangible criteria. The objective of this paper is to present a new MCDM technique based on fuzzy set theory and VIKOR method for evaluating HCW disposal methods. Linguistic variables are used by decision makers to assess the ratings and weights for the established criteria. The ordered weighted averaging (OWA) operator is utilized to aggregate individual opinions of decision makers into a group assessment. The computational procedure of the proposed framework is illustrated through a case study in Shanghai, one of the largest cities of China. The HCW treatment alternatives considered in this study include “incineration”, “steam sterilization”, “microwave” and “landfill”. The results obtained using the proposed approach are analyzed in a comparative way.

  6. Assessment of health-care waste disposal methods using a VIKOR-based fuzzy multi-criteria decision making method

    International Nuclear Information System (INIS)

    Liu, Hu-Chen; Wu, Jing; Li, Ping

    2013-01-01

    Highlights: • Propose a VIKOR-based fuzzy MCDM technique for evaluating HCW disposal methods. • Linguistic variables are used to assess the ratings and weights for the criteria. • The OWA operator is utilized to aggregate individual opinions of decision makers. • A case study is given to illustrate the procedure of the proposed framework. - Abstract: Nowadays selection of the appropriate treatment method in health-care waste (HCW) management has become a challenge task for the municipal authorities especially in developing countries. Assessment of HCW disposal alternatives can be regarded as a complicated multi-criteria decision making (MCDM) problem which requires consideration of multiple alternative solutions and conflicting tangible and intangible criteria. The objective of this paper is to present a new MCDM technique based on fuzzy set theory and VIKOR method for evaluating HCW disposal methods. Linguistic variables are used by decision makers to assess the ratings and weights for the established criteria. The ordered weighted averaging (OWA) operator is utilized to aggregate individual opinions of decision makers into a group assessment. The computational procedure of the proposed framework is illustrated through a case study in Shanghai, one of the largest cities of China. The HCW treatment alternatives considered in this study include “incineration”, “steam sterilization”, “microwave” and “landfill”. The results obtained using the proposed approach are analyzed in a comparative way

  7. Metric Sex Determination of the Human Coxal Bone on a Virtual Sample using Decision Trees.

    Science.gov (United States)

    Savall, Frédéric; Faruch-Bilfeld, Marie; Dedouit, Fabrice; Sans, Nicolas; Rousseau, Hervé; Rougé, Daniel; Telmon, Norbert

    2015-11-01

    Decision trees provide an alternative to multivariate discriminant analysis, which is still the most commonly used in anthropometric studies. Our study analyzed the metric characterization of a recent virtual sample of 113 coxal bones using decision trees for sex determination. From 17 osteometric type I landmarks, a dataset was built with five classic distances traditionally reported in the literature and six new distances selected using the two-step ratio method. A ten-fold cross-validation was performed, and a decision tree was established on two subsamples (training and test sets). The decision tree established on the training set included three nodes and its application to the test set correctly classified 92% of individuals. This percentage was similar to the data of the literature. The usefulness of decision trees has been demonstrated in numerous fields. They have been already used in sex determination, body mass prediction, and ancestry estimation. This study shows another use of decision trees enabling simple and accurate sex determination. © 2015 American Academy of Forensic Sciences.

  8. Development of a New Decision Tree to Rapidly Screen Chemical Estrogenic Activities of Xenopus laevis.

    Science.gov (United States)

    Wang, Ting; Li, Weiying; Zheng, Xiaofeng; Lin, Zhifen; Kong, Deyang

    2014-02-01

    During the last past decades, there is an increasing number of studies about estrogenic activities of the environmental pollutants on amphibians and many determination methods have been proposed. However, these determination methods are time-consuming and expensive, and a rapid and simple method to screen and test the chemicals for estrogenic activities to amphibians is therefore imperative. Herein is proposed a new decision tree formulated not only with physicochemical parameters but also a biological parameter that was successfully used to screen estrogenic activities of the chemicals on amphibians. The biological parameter, CDOCKER interaction energy (Ebinding ) between chemicals and the target proteins was calculated based on the method of molecular docking, and it was used to revise the decision tree formulated by Hong only with physicochemical parameters for screening estrogenic activity of chemicals in rat. According to the correlation between Ebinding of rat and Xenopus laevis, a new decision tree for estrogenic activities in Xenopus laevis is finally proposed. Then it was validated by using the randomly 8 chemicals which can be frequently exposed to Xenopus laevis, and the agreement between the results from the new decision tree and the ones from experiments is generally satisfactory. Consequently, the new decision tree can be used to screen the estrogenic activities of the chemicals, and combinational use of the Ebinding and classical physicochemical parameters can greatly improves Hong's decision tree. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. Implementation of Data Mining to Analyze Drug Cases Using C4.5 Decision Tree

    Science.gov (United States)

    Wahyuni, Sri

    2018-03-01

    Data mining was the process of finding useful information from a large set of databases. One of the existing techniques in data mining was classification. The method used was decision tree method and algorithm used was C4.5 algorithm. The decision tree method was a method that transformed a very large fact into a decision tree which was presenting the rules. Decision tree method was useful for exploring data, as well as finding a hidden relationship between a number of potential input variables with a target variable. The decision tree of the C4.5 algorithm was constructed with several stages including the selection of attributes as roots, created a branch for each value and divided the case into the branch. These stages would be repeated for each branch until all the cases on the branch had the same class. From the solution of the decision tree there would be some rules of a case. In this case the researcher classified the data of prisoners at Labuhan Deli prison to know the factors of detainees committing criminal acts of drugs. By applying this C4.5 algorithm, then the knowledge was obtained as information to minimize the criminal acts of drugs. From the findings of the research, it was found that the most influential factor of the detainee committed the criminal act of drugs was from the address variable.

  10. Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

    Directory of Open Access Journals (Sweden)

    Chao Yang

    2017-11-01

    Full Text Available Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D Terrain model, which was created using an image fusion digital elevation model (DEM, to select training samples (ROIs, and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications.

  11. Total Path Length and Number of Terminal Nodes for Decision Trees

    KAUST Repository

    Hussain, Shahid

    2014-09-13

    This paper presents a new tool for study of relationships between total path length (average depth) and number of terminal nodes for decision trees. These relationships are important from the point of view of optimization of decision trees. In this particular case of total path length and number of terminal nodes, the relationships between these two cost functions are closely related with space-time trade-off. In addition to algorithm to compute the relationships, the paper also presents results of experiments with datasets from UCI ML Repository1. These experiments show how two cost functions behave for a given decision table and the resulting plots show the Pareto frontier or Pareto set of optimal points. Furthermore, in some cases this Pareto frontier is a singleton showing the total optimality of decision trees for the given decision table.

  12. A fuzzy multi-criteria decision model for integrated suppliers selection and optimal order allocation in the green supply chain

    Directory of Open Access Journals (Sweden)

    Hamzeh Amin-Tahmasbi

    2018-09-01

    Full Text Available Today, with the advancement of technology in the production process of various products, the achievement of sustainable production and development has become one of the main concerns of factories and manufacturing organizations. In the same vein, many manufacturers try to select suppliers in their upstream supply chains that have the best performance in terms of sustainable development criteria. In this research, a new multi-criteria decision-making model for selecting suppliers and assigning orders in the green supply chain is presented with a fuzzy optimization approach. Due to uncertainty in supplier capacity as well as customer demand, the problem is formulated as a fuzzy multi-objective linear programming (FMOLP. The proposed model for the selection of suppliers of SAPCO Corporation is evaluated. Firstly, in order to select and rank suppliers in a green supply chain, a network structure of criteria has defined with five main criteria of cost, quality, delivery, technology and environmental benefits. Subsequently, using incomplete fuzzy linguistic relationships, pair-wise comparisons between the criteria and sub-criteria as well as the operation of the options will be assessed. The results of these comparisons rank the existing suppliers in terms of performance and determine the utility of them. The output of these calculations (utility index is used in the optimization model. Subsequently, in the order allocation process, the two functions of the target cost of purchase and purchase value are optimized simultaneously. Finally, the order quantity is determined for each supplier in each period.

  13. Predicting gene function using hierarchical multi-label decision tree ensembles

    Directory of Open Access Journals (Sweden)

    Kocev Dragi

    2010-01-01

    Full Text Available Abstract Background S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability. Results We study the use of decision tree based models for predicting the multiple functions of ORFs. First, we describe an algorithm for learning hierarchical multi-label decision trees. These can simultaneously predict all the functions of an ORF, while respecting a given hierarchy of gene functions (such as FunCat or GO. We present new results obtained with this algorithm, showing that the trees found by it exhibit clearly better predictive performance than the trees found by previously described methods. Nevertheless, the predictive performance of individual trees is lower than that of some recently proposed statistical learning methods. We show that ensembles of such trees are more accurate than single trees and are competitive with state-of-the-art statistical learning and functional linkage methods. Moreover, the ensemble method is computationally efficient and easy to use. Conclusions Our results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.

  14. Development of a diagnostic decision tree for obstructive pulmonary diseases based on real-life data

    Directory of Open Access Journals (Sweden)

    Esther I. Metting

    2016-01-01

    Full Text Available The aim of this study was to develop and explore the diagnostic accuracy of a decision tree derived from a large real-life primary care population. Data from 9297 primary care patients (45% male, mean age 53±17 years with suspicion of an obstructive pulmonary disease was derived from an asthma/chronic obstructive pulmonary disease (COPD service where patients were assessed using spirometry, the Asthma Control Questionnaire, the Clinical COPD Questionnaire, history data and medication use. All patients were diagnosed through the Internet by a pulmonologist. The Chi-squared Automatic Interaction Detection method was used to build the decision tree. The tree was externally validated in another real-life primary care population (n=3215. Our tree correctly diagnosed 79% of the asthma patients, 85% of the COPD patients and 32% of the asthma–COPD overlap syndrome (ACOS patients. External validation showed a comparable pattern (correct: asthma 78%, COPD 83%, ACOS 24%. Our decision tree is considered to be promising because it was based on real-life primary care patients with a specialist's diagnosis. In most patients the diagnosis could be correctly predicted. Predicting ACOS, however, remained a challenge. The total decision tree can be implemented in computer-assisted diagnostic systems for individual patients. A simplified version of this tree can be used in daily clinical practice as a desk tool.

  15. Development of a diagnostic decision tree for obstructive pulmonary diseases based on real-life data

    Science.gov (United States)

    in ’t Veen, Johannes C.C.M.; Dekhuijzen, P.N. Richard; van Heijst, Ellen; Kocks, Janwillem W.H.; Muilwijk-Kroes, Jacqueline B.; Chavannes, Niels H.; van der Molen, Thys

    2016-01-01

    The aim of this study was to develop and explore the diagnostic accuracy of a decision tree derived from a large real-life primary care population. Data from 9297 primary care patients (45% male, mean age 53±17 years) with suspicion of an obstructive pulmonary disease was derived from an asthma/chronic obstructive pulmonary disease (COPD) service where patients were assessed using spirometry, the Asthma Control Questionnaire, the Clinical COPD Questionnaire, history data and medication use. All patients were diagnosed through the Internet by a pulmonologist. The Chi-squared Automatic Interaction Detection method was used to build the decision tree. The tree was externally validated in another real-life primary care population (n=3215). Our tree correctly diagnosed 79% of the asthma patients, 85% of the COPD patients and 32% of the asthma–COPD overlap syndrome (ACOS) patients. External validation showed a comparable pattern (correct: asthma 78%, COPD 83%, ACOS 24%). Our decision tree is considered to be promising because it was based on real-life primary care patients with a specialist's diagnosis. In most patients the diagnosis could be correctly predicted. Predicting ACOS, however, remained a challenge. The total decision tree can be implemented in computer-assisted diagnostic systems for individual patients. A simplified version of this tree can be used in daily clinical practice as a desk tool. PMID:27730177

  16. Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis.

    Science.gov (United States)

    Panje, Cédric M; Glatzer, Markus; von Rappard, Joscha; Rothermundt, Christian; Hundsberger, Thomas; Zumstein, Valentin; Plasswilm, Ludwig; Putora, Paul Martin

    2017-08-16

    The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously. Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators. The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis. This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.

  17. Boundary expansion algorithm of a decision tree induction for an imbalanced dataset

    Directory of Open Access Journals (Sweden)

    Kesinee Boonchuay

    2017-10-01

    Full Text Available A decision tree is one of the famous classifiers based on a recursive partitioning algorithm. This paper introduces the Boundary Expansion Algorithm (BEA to improve a decision tree induction that deals with an imbalanced dataset. BEA utilizes all attributes to define non-splittable ranges. The computed means of all attributes for minority instances are used to find the nearest minority instance, which will be expanded along all attributes to cover a minority region. As a result, BEA can successfully cope with an imbalanced dataset comparing with C4.5, Gini, asymmetric entropy, top-down tree, and Hellinger distance decision tree on 25 imbalanced datasets from the UCI Repository.

  18. Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines.

    Science.gov (United States)

    Lee, Saro; Park, Inhye

    2013-09-30

    Subsidence of ground caused by underground mines poses hazards to human life and property. This study analyzed the hazard to ground subsidence using factors that can affect ground subsidence and a decision tree approach in a geographic information system (GIS). The study area was Taebaek, Gangwon-do, Korea, where many abandoned underground coal mines exist. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 50/50 for training and validation of the models. A data-mining classification technique was applied to the GSH mapping, and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The frequency ratio model was also applied to the GSH mapping for comparing with probabilistic model. The resulting GSH maps were validated using area-under-the-curve (AUC) analysis with the subsidence area data that had not been used for training the model. The highest accuracy was achieved by the decision tree model using CHAID algorithm (94.01%) comparing with QUEST algorithms (90.37%) and frequency ratio model (86.70%). These accuracies are higher than previously reported results for decision tree. Decision tree methods can therefore be used efficiently for GSH analysis and might be widely used for prediction of various spatial events. Copyright © 2013. Published by Elsevier Ltd.

  19. MRI-based decision tree model for diagnosis of biliary atresia.

    Science.gov (United States)

    Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung

    2018-02-23

    To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.

  20. Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis.

    Science.gov (United States)

    Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H

    2016-01-01

    Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.

  1. The Application of a Decision-making Approach based on Fuzzy ANP and TOPSIS for Selecting a Strategic Supplier

    Directory of Open Access Journals (Sweden)

    Rajesri Govindaraju

    2015-09-01

    Full Text Available Supplier selection becomes very important when used in the context of strategic partnerships because of the long-term orientation of the relationship. This paper describes the application of a decision-making approach for selecting a strategic partner (supplier. The approach starts with defining a set of criteria that fits the company’s condition. In the next steps, a combination of fuzzy-ANP and TOPSIS methods is used to determine the weight for each criterion and rank all the alternatives. The application of the approach in an Indonesian manufacturing company showed that the three factors that got the highest weight were “geographical location”, “current operating performance”, and “reliability”. Geographical location got the highest weight because it affects many other factors such as reaction to changes in demand, after-sales service, and delivery lead-time. Application of the approach helps decision-makers to gain effectiveness and efficiency in the decision-making process because it facilitates them to express their group’s collective preferences while also providing opportunities for members to express their individual preferences. Future research can be directed at combining qualitative and quantitative criteria to develop the best criteria and methods for the selection of the best suppliers based on fuzzy ANP and TOPSIS.

  2. Decision tree analysis to evaluate dry cow strategies under UK conditions

    NARCIS (Netherlands)

    Berry, E.A.; Hogeveen, H.; Hillerton, J.E.

    2004-01-01

    Economic decisions on animal health strategies address the cost-benefit aspect along with animal welfare and public health concerns. Decision tree analysis at an individual cow level highlighted that there is little economic difference between the use of either dry cow antibiotic or an internal teat

  3. Visualization of Decision Tree State for the Classification of Parkinson's Disease

    NARCIS (Netherlands)

    Valentijn, E

    2016-01-01

    Decision trees have been shown to be effective at classifying subjects with Parkinson’s disease when provided with features (subject scores) derived from FDG-PET data. Such subject scores have strong discriminative power but are not intuitive to understand. We therefore augment each decision node

  4. Assessing School Readiness for a Practice Arrangement Using Decision Tree Methodology.

    Science.gov (United States)

    Barger, Sara E.

    1998-01-01

    Questions in a decision-tree address mission, faculty interest, administrative support, and practice plan as a way of assessing arrangements for nursing faculty's clinical practice. Decisions should be based on congruence between the human resource allocation and the reward systems. (SK)

  5. Using decision tree induction systems for modeling space-time behavior

    NARCIS (Netherlands)

    Arentze, T.A.; Hofman, F.; Mourik, van H.; Timmermans, H.J.P.; Wets, G.

    2000-01-01

    Discrete choice models are commonly used to predict individuals' activity and travel choices either separately or simultaneously in activity scheduling models. This paper investigates the possibilities of decision tree induction systems as an alternative approach. The ability of decision frees to

  6. Decision table development and application to the construction of fault trees

    International Nuclear Information System (INIS)

    Salem, S.L.; Wu, J.S.; Apostolakis, G.

    1979-01-01

    A systematic methodology for the construction of fault trees based on the use of decision tables has been developed. These tables are used to describe each possible output state of a component as a set of combinations of states of inputs and internal operational or T states. Two methods for modeling component behavior via decision tables have been developed, one inductive and one deductive. These methods are useful for creating decision tables that realistically model the operational and failure modes of electrical, mechanical, and hydraulic components as well as human interactions inhibit conditions and common-cause events. A computer code CAT (Computer Automated Tree) has been developed to automatically produce fault trees from decision tables. A simple electrical system was chosen to illustrate the basic features of the decision table approach and to provide an example of an actual fault tree produced by this code. This example demonstrates the potential utility of such an automated approach to fault tree construction once a basic set of general decision tables has been developed

  7. [Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

    Science.gov (United States)

    Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

    2012-02-01

    In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

  8. Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data.

    Science.gov (United States)

    Barros, Rodrigo C; Winck, Ana T; Machado, Karina S; Basgalupp, Márcio P; de Carvalho, André C P L F; Ruiz, Duncan D; de Souza, Osmar Norberto

    2012-11-21

    This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

  9. The Studies of Decision Tree in Estimation of Breast Cancer Risk by Using Polymorphism Nucleotide

    Directory of Open Access Journals (Sweden)

    Frida Seyedmir

    2017-07-01

    Full Text Available Abstract Introduction:   Decision tree is the data mining tools to collect, accurate prediction and sift information from massive amounts of data that are used widely in the field of computational biology and bioinformatics. In bioinformatics can be predict on diseases, including breast cancer. The use of genomic data including single nucleotide polymorphisms is a very important factor in predicting the risk of diseases. The number of seven important SNP among hundreds of thousands genetic markers were identified as factors associated with breast cancer. The objective of this study is to evaluate the training data on decision tree predictor error of the risk of breast cancer by using single nucleotide polymorphism genotype. Methods: The risk of breast cancer were calculated associated with the use of SNP formula:xj = fo * In human,  The decision tree can be used To predict the probability of disease using single nucleotide polymorphisms .Seven SNP with different odds ratio associated with breast cancer considered and coding and design of decision tree model, C4.5, by  Csharp2013 programming language were done. In the decision tree created with the coding, the four important associated SNP was considered. The decision tree error in two case of coding and using WEKA were assessment and percentage of decision tree accuracy in prediction of breast cancer were calculated. The number of trained samples was obtained with systematic sampling. With coding, two scenarios as well as software WEKA, three scenarios with different sets of data and the number of different learning and testing, were evaluated. Results: In both scenarios of coding, by increasing the training percentage from 66/66 to 86/42, the error reduced from 55/56 to 9/09. Also by running of WEKA on three scenarios with different sets of data, the number of different education, and different tests by increasing records number from 81 to 2187, the error rate decreased from 48/15 to 13

  10. Multivariate analysis of flow cytometric data using decision trees

    Directory of Open Access Journals (Sweden)

    Svenja eSimon

    2012-04-01

    Full Text Available Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called 'induction of decision trees' in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees's quality, we used stratified 5-fold cross-validation and chose the 'best' tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

  11. Combining evolutionary algorithms with oblique decision trees to detect bent-double galaxies

    Science.gov (United States)

    Cantu-Paz, Erick; Kamath, Chandrika

    2000-10-01

    Decision tress have long been popular in classification as they use simple and easy-to-understand tests at each node. Most variants of decision trees test a single attribute at a node, leading to axis- parallel trees, where the test results in a hyperplane which is parallel to one of the dimensions in the attribute space. These trees can be rather large and inaccurate in cases where the concept to be learned is best approximated by oblique hyperplanes. In such cases, it may be more appropriate to use an oblique decision tree, where the decision at each node is a linear combination of the attributes. Oblique decision trees have not gained wide popularity in part due to the complexity of constructing good oblique splits and the tendency of existing splitting algorithms to get stuck in local minima. Several alternatives have been proposed to handle these problems including randomization in conjunction wiht deterministic hill-climbing and the use of simulated annealing. In this paper, we use evolutionary algorithms (EAs) to determine the split. EAs are well suited for this problem because of their global search properties, their tolerance to noisy fitness evaluations, and their scalability to large dimensional search spaces. We demonstrate our technique on a synthetic data set, and then we apply it to a practical problem from astronomy, namely, the classification of galaxies with a bent-double morphology. In addition, we describe our experiences with several split evaluation criteria. Our results suggest that, in some cases, the evolutionary approach is faster and more accurate than existing oblique decision tree algorithms. However, for our astronomical data, the accuracy is not significantly different than the axis-parallel trees.

  12. Recurrent fuzzy ranking methods

    Science.gov (United States)

    Hajjari, Tayebeh

    2012-11-01

    With the increasing development of fuzzy set theory in various scientific fields and the need to compare fuzzy numbers in different areas. Therefore, Ranking of fuzzy numbers plays a very important role in linguistic decision-making, engineering, business and some other fuzzy application systems. Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce non-intuitive results in certain case. In this paper, we reviewed some recent ranking methods, which will be useful for the researchers who are interested in this area.

  13. Using Decision Trees to Characterize Verbal Communication During Change and Stuck Episodes in the Therapeutic Process

    Directory of Open Access Journals (Sweden)

    Víctor Hugo eMasías

    2015-04-01

    Full Text Available Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBtree, and REPtree are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1,760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice.

  14. [Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus].

    Science.gov (United States)

    Kleinhans, Sonja; Herrmann, Eva; Kohnen, Thomas; Bühren, Jens

    2017-08-15

    Background Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. Patients and Methods The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. Results Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. Conclusion On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus. Georg Thieme Verlag KG Stuttgart · New York.

  15. Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process.

    Science.gov (United States)

    Masías, Víctor H; Krause, Mariane; Valdés, Nelson; Pérez, J C; Laengle, Sigifredo

    2015-01-01

    Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice.

  16. PCA based feature reduction to improve the accuracy of decision tree c4.5 classification

    Science.gov (United States)

    Nasution, M. Z. F.; Sitompul, O. S.; Ramli, M.

    2018-03-01

    Splitting attribute is a major process in Decision Tree C4.5 classification. However, this process does not give a significant impact on the establishment of the decision tree in terms of removing irrelevant features. It is a major problem in decision tree classification process called over-fitting resulting from noisy data and irrelevant features. In turns, over-fitting creates misclassification and data imbalance. Many algorithms have been proposed to overcome misclassification and overfitting on classifications Decision Tree C4.5. Feature reduction is one of important issues in classification model which is intended to remove irrelevant data in order to improve accuracy. The feature reduction framework is used to simplify high dimensional data to low dimensional data with non-correlated attributes. In this research, we proposed a framework for selecting relevant and non-correlated feature subsets. We consider principal component analysis (PCA) for feature reduction to perform non-correlated feature selection and Decision Tree C4.5 algorithm for the classification. From the experiments conducted using available data sets from UCI Cervical cancer data set repository with 858 instances and 36 attributes, we evaluated the performance of our framework based on accuracy, specificity and precision. Experimental results show that our proposed framework is robust to enhance classification accuracy with 90.70% accuracy rates.

  17. Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture.

    Science.gov (United States)

    Chen, Yingyi; Zhen, Zhumi; Yu, Huihui; Xu, Jing

    2017-01-14

    In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.

  18. Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT for Aquaculture

    Directory of Open Access Journals (Sweden)

    Yingyi Chen

    2017-01-01

    Full Text Available In the Internet of Things (IoT equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.

  19. Dual Processes in Decision Making and Developmental Neuroscience: A Fuzzy-Trace Model

    Science.gov (United States)

    Reyna, Valerie F.; Brainerd, Charles J.

    2011-01-01

    From Piaget to the present, traditional and dual-process theories have predicted improvement in reasoning from childhood to adulthood, and improvement has been observed. However, developmental reversals--that reasoning biases emerge with development--have also been observed in a growing list of paradigms. We explain how fuzzy-trace theory predicts…

  20. Outsourcing the Portal: Another Branch in the Decision Tree.

    Science.gov (United States)

    McMahon, Tim

    2000-01-01

    Discussion of the management of information resources in organizations focuses on the use of portal technologies to update intranet capabilities. Considers application outsourcing decisions, reviews benefits (including reducing costs) as well as concerns, and describes application service providers (ASPs). (LRW)

  1. A fuzzy approach to a multiple criteria and geographical information system for decision support on suitable locations for biogas plants

    DEFF Research Database (Denmark)

    Franco de los Rios, Camilo Andres; Bojesen, Mikkel; Hougaard, Jens Leth

    The purpose of this paper is to model the multi-criteria decision problem of identifying the most suitable facility locations for biogas plants under an integrated decision support methodology. Here the Geographical Information System (GIS) is used for measuring the attributes of the alternatives...... according to a given set of criteria. Measurements are taken in interval form, expressing the natural imprecision of common data, and the Fuzzy Weighted Overlap Dominance (FWOD) procedure is applied for aggregating and exploiting this kind of data, obtaining suitability degrees for every alternative....... The estimation of criteria weights, which is necessary for applying the FWOD procedure, is done by means of the Analytical Hierarchy Process (AHP), such that a combined AHP-FWOD methodology allows identifying the more suitable sites for building biogas plants. We show that the FWOD relevance-ranking procedure...

  2. An enhanced component connection method for conversion of fault trees to binary decision diagrams

    International Nuclear Information System (INIS)

    Remenyte-Prescott, R.; Andrews, J.D.

    2008-01-01

    Fault tree analysis (FTA) is widely applied to assess the failure probability of industrial systems. Many computer packages are available, which are based on conventional kinetic tree theory methods. When dealing with large (possibly non-coherent) fault trees, the limitations of the technique in terms of accuracy of the solutions and the efficiency of the processing time become apparent. Over recent years, the binary decision diagram (BDD) method has been developed that solves fault trees and overcomes the disadvantages of the conventional FTA approach. First of all, a fault tree for a particular system failure mode is constructed and then converted to a BDD for analysis. This paper analyses alternative methods for the fault tree to BDD conversion process. For most fault tree to BDD conversion approaches, the basic events of the fault tree are placed in an ordering. This can dramatically affect the size of the final BDD and the success of qualitative and quantitative analyses of the system. A set of rules is then applied to each gate in the fault tree to generate the BDD. An alternative approach can also be used, where BDD constructs for each of the gate types are first built and then merged to represent a parent gate. A powerful and efficient property, sub-node sharing, is also incorporated in the enhanced method proposed in this paper. Finally, a combined approach is developed taking the best features of the alternative methods. The efficiency of the techniques is analysed and discussed

  3. Post-event human decision errors: operator action tree/time reliability correlation

    Energy Technology Data Exchange (ETDEWEB)

    Hall, R E; Fragola, J; Wreathall, J

    1982-11-01

    This report documents an interim framework for the quantification of the probability of errors of decision on the part of nuclear power plant operators after the initiation of an accident. The framework can easily be incorporated into an event tree/fault tree analysis. The method presented consists of a structure called the operator action tree and a time reliability correlation which assumes the time available for making a decision to be the dominating factor in situations requiring cognitive human response. This limited approach decreases the magnitude and complexity of the decision modeling task. Specifically, in the past, some human performance models have attempted prediction by trying to emulate sequences of human actions, or by identifying and modeling the information processing approach applicable to the task. The model developed here is directed at describing the statistical performance of a representative group of hypothetical individuals responding to generalized situations.

  4. Post-event human decision errors: operator action tree/time reliability correlation

    International Nuclear Information System (INIS)

    Hall, R.E.; Fragola, J.; Wreathall, J.

    1982-11-01

    This report documents an interim framework for the quantification of the probability of errors of decision on the part of nuclear power plant operators after the initiation of an accident. The framework can easily be incorporated into an event tree/fault tree analysis. The method presented consists of a structure called the operator action tree and a time reliability correlation which assumes the time available for making a decision to be the dominating factor in situations requiring cognitive human response. This limited approach decreases the magnitude and complexity of the decision modeling task. Specifically, in the past, some human performance models have attempted prediction by trying to emulate sequences of human actions, or by identifying and modeling the information processing approach applicable to the task. The model developed here is directed at describing the statistical performance of a representative group of hypothetical individuals responding to generalized situations

  5. An Efficient Method of Vibration Diagnostics For Rotating Machinery Using a Decision Tree

    Directory of Open Access Journals (Sweden)

    Bo Suk Yang

    2000-01-01

    Full Text Available This paper describes an efficient method to automatize vibration diagnosis for rotating machinery using a decision tree, which is applicable to vibration diagnosis expert system. Decision tree is a widely known formalism for expressing classification knowledge and has been used successfully in many diverse areas such as character recognition, medical diagnosis, and expert systems, etc. In order to build a decision tree for vibration diagnosis, we have to define classes and attributes. A set of cases based on past experiences is also needed. This training set is inducted using a result-cause matrix newly developed in the present work instead of using a conventionally implemented cause-result matrix. This method was applied to diagnostics for various cases taken from published work. It is found that the present method predicts causes of the abnormal vibration for test cases with high reliability.

  6. A symptom based decision tree approach to boiling water reactor emergency operating procedures

    International Nuclear Information System (INIS)

    Knobel, R.C.

    1984-01-01

    This paper describes a Decision Tree approach to development of BWR Emergency Operating Procedures for use by operators during emergencies. This approach utilizes the symptom based Emergency Procedure Guidelines approved for implementation by the USNRC. Included in the paper is a discussion of the relative merits of the event based Emergency Operating Procedures currently in use at USBWR plants. The body of the paper is devoted to a discussion of the Decision Tree Approach to Emergency Operating Procedures soon to be implemented at two United States Boiling Water Reactor plants, why this approach solves many of the problems with procedures indentified in the post accident reviews of Three Mile Island procedures, and why only now is this approach both desirable and feasible. The paper discusses how nuclear plant simulators were involved in the development of the Emergency Operating Procedure decision trees, and in the verification and validation of these procedures. (orig./HP)

  7. Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

    Science.gov (United States)

    Lee, Hanbong

    2017-01-01

    In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately.

  8. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

    KAUST Repository

    Busbait, Monther I.

    2014-05-01

    We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum depth of decision tree for diagnosis of constant faults depending on the number of edges in a contact network over that basis. Also, we obtain asymptotic bounds on the depth of decision trees for diagnosis of each type of constant faults depending on the number of edges in contact networks in the worst case per basis. We study the set of indecomposable contact networks with up to 10 edges and obtain sharp coefficients for the linear upper bound for diagnosis of constant faults in contact networks over bases of these indecomposable contact networks. We use a set of algorithms, including one that we create, to obtain the sharp coefficients.

  9. Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification.

    Science.gov (United States)

    Zhao, Yang; Zheng, Wei; Zhuo, Daisy Y; Lu, Yuefeng; Ma, Xiwen; Liu, Hengchang; Zeng, Zhen; Laird, Glen

    2017-10-11

    Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

  10. Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy.

    Science.gov (United States)

    Surucu, Murat; Shah, Karan K; Mescioglu, Ibrahim; Roeske, John C; Small, William; Choi, Mehee; Emami, Bahman

    2016-02-01

    To develop decision trees predicting for tumor volume reduction in patients with head and neck (H&N) cancer using pretreatment clinical and pathological parameters. Forty-eight patients treated with definitive concurrent chemoradiotherapy for squamous cell carcinoma of the nasopharynx, oropharynx, oral cavity, or hypopharynx were retrospectively analyzed. These patients were rescanned at a median dose of 37.8 Gy and replanned to account for anatomical changes. The percentages of gross tumor volume (GTV) change from initial to rescan computed tomography (CT; %GTVΔ) were calculated. Two decision trees were generated to correlate %GTVΔ in primary and nodal volumes with 14 characteristics including age, gender, Karnofsky performance status (KPS), site, human papilloma virus (HPV) status, tumor grade, primary tumor growth pattern (endophytic/exophytic), tumor/nodal/group stages, chemotherapy regimen, and primary, nodal, and total GTV volumes in the initial CT scan. The C4.5 Decision Tree induction algorithm was implemented. The median %GTVΔ for primary, nodal, and total GTVs was 26.8%, 43.0%, and 31.2%, respectively. Type of chemotherapy, age, primary tumor growth pattern, site, KPS, and HPV status were the most predictive parameters for primary %GTVΔ decision tree, whereas for nodal %GTVΔ, KPS, site, age, primary tumor growth pattern, initial primary GTV, and total GTV volumes were predictive. Both decision trees had an accuracy of 88%. There can be significant changes in primary and nodal tumor volumes during the course of H&N chemoradiotherapy. Considering the proposed decision trees, radiation oncologists can select patients predicted to have high %GTVΔ, who would theoretically gain the most benefit from adaptive radiotherapy, in order to better use limited clinical resources. © The Author(s) 2015.

  11. [Analysis of the characteristics of the older adults with depression using data mining decision tree analysis].

    Science.gov (United States)

    Park, Myonghwa; Choi, Sora; Shin, A Mi; Koo, Chul Hoi

    2013-02-01

    The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

  12. A Multi Criteria Group Decision-Making Model for Teacher Evaluation in Higher Education Based on Cloud Model and Decision Tree

    Science.gov (United States)

    Chang, Ting-Cheng; Wang, Hui

    2016-01-01

    This paper proposes a cloud multi-criteria group decision-making model for teacher evaluation in higher education which is involving subjectivity, imprecision and fuzziness. First, selecting the appropriate evaluation index depending on the evaluation objectives, indicating a clear structural relationship between the evaluation index and…

  13. Identification of radon anomalies in soil gas using decision trees and neural networks

    International Nuclear Information System (INIS)

    Zmazek, B.; Dzeroski, S.; Torkar, D.; Vaupotic, J.; Kobal, I.

    2010-01-01

    The time series of radon ( 222 Rn) concentration in soil gas at a fault, together with the environmental parameters, have been analysed applying two machine learning techniques: (I) decision trees and (II) neural networks, with the aim at identifying radon anomalies caused by seismic events and not simply ascribed to the effect of the environmental parameters. By applying neural networks, 10 radon anomalies were observed for 12 earthquakes, while with decision trees, the anomaly was found for every earthquake, but, undesirably, some anomalies appeared also during periods without earthquakes. (authors)

  14. Decision tree based knowledge acquisition and failure diagnosis using a PWR loop vibration model

    International Nuclear Information System (INIS)

    Bauernfeind, V.; Ding, Y.

    1993-01-01

    An analytical vibration model of the primary system of a 1300 MW PWR was used for simulating mechanical faults. Deviations in the calculated power density spectra and coherence functions are determined and classified. The decision tree technique is then used for a personal computer supported knowledge presentation and for optimizing the logical relationships between the simulated faults and the observed symptoms. The optimized decision tree forms the knowledge base and can be used to diagnose known cases as well as to include new data into the knowledge base if new faults occur. (author)

  15. Vlsi implementation of flexible architecture for decision tree classification in data mining

    Science.gov (United States)

    Sharma, K. Venkatesh; Shewandagn, Behailu; Bhukya, Shankar Nayak

    2017-07-01

    The Data mining algorithms have become vital to researchers in science, engineering, medicine, business, search and security domains. In recent years, there has been a terrific raise in the size of the data being collected and analyzed. Classification is the main difficulty faced in data mining. In a number of the solutions developed for this problem, most accepted one is Decision Tree Classification (DTC) that gives high precision while handling very large amount of data. This paper presents VLSI implementation of flexible architecture for Decision Tree classification in data mining using c4.5 algorithm.

  16. Decision aid by fuzzy inference: a case study related to the problem of radioactive waste management

    International Nuclear Information System (INIS)

    Krunsch, P.; Fiordalisa, A.; Fortemps, Ph.

    1999-01-01

    This paper illustrates a fuzzy inference system (FIS) developed to assist the economic calculus in radioactive waste management (RWM). The extended time horizons and, in addition, the first-of-a-kind nature of many RWM systems induce large cost uncertainties in project funding. The traditional approach in economic calculus is to include contingency factors in basic cost estimates. A distinction is made between T-factors, used for technological uncertainties, and P-factors, used for project contingencies. In the particular case of nuclear projects, the Electric Power Research Institute (EPRI) has developed specific recommendations for defining both contingency factors. As a generalisation of the EPRI results, a new methodology using fuzzy inference rules is proposed. The inputs to the FIS are derived from the answers of experts regarding both the degrees of technological maturity and project advancement. Inferred T- and P-factors proposed by the FIS are given either as single estimates as possibility intervals. (authors)

  17. Modeling entrepreneurial decision-making process using concepts from fuzzy set theory

    OpenAIRE

    Khefacha, Islem; Belkacem, Lotfi

    2015-01-01

    Entrepreneurship and entrepreneurial culture are receiving an increased amount of attention in both academic research and practice. The different fields of study have focused on the analysis of the characteristics of potential entrepreneurs and the firm-creation process. In this paper, we develop and test an economic-psychological model of factors that influence individuals' intentions to go into business. We introduce a new measure of entrepreneurial intention based on the logic fuzzy techni...

  18. Assessment of air quality in Haora River basin using fuzzy multiple-attribute decision making techniques.

    Science.gov (United States)

    Singh, Ajit Pratap; Chakrabarti, Sumanta; Kumar, Sumit; Singh, Anjaney

    2017-08-01

    This paper deals with assessment of air quality in Haora River basin using two techniques. Initially, air quality indices were evaluated using a modified EPA method. The indices were also evaluated using a fuzzy comprehensive assessment (FCA) method. The results obtained from the fuzzy comprehensive assessment method were compared to that obtained from the modified EPA method. To illustrate the applicability of the methodology proposed herein, a case study has been presented. Air samples have been collected at 10 sampling sites located along Haora River. Six important air pollutants, namely, carbon monoxide, sulfur dioxide, nitrogen dioxide, suspended particulate matter (SPM), PM 10 , and lead, were monitored continuously, and air quality maps were generated on the GIS platform. Comparison of the methodologies has clearly highlighted superiority and robustness of the fuzzy comprehensive assessment method in determining air quality indices under study. It has effectively addressed the inherent uncertainties involved in the evaluation, modeling, and interpretation of sampling data, which was beyond the scope of the traditional weighted approaches employed otherwise. The FCA method is robust and prepares a credible platform of air quality evaluation and identification, in face of the uncertainties that remain eclipsed in the traditional approaches like the modified EPA method. The insights gained through the present study are believed to be of pivotal significance in guiding the development and implementation of effective environmental remedial action plans in the study area.

  19. Dynamic Fuzzy Clustering Method for Decision Support in Electricity Markets Negotiation

    Directory of Open Access Journals (Sweden)

    Ricardo FAIA

    2016-10-01

    Full Text Available Artificial Intelligence (AI methods contribute to the construction of systems where there is a need to automate the tasks. They are typically used for problems that have a large response time, or when a mathematical method cannot be used to solve the problem. However, the application of AI brings an added complexity to the development of such applications. AI has been frequently applied in the power systems field, namely in Electricity Markets (EM. In this area, AI applications are essentially used to forecast / estimate the prices of electricity or to search for the best opportunity to sell the product. This paper proposes a clustering methodology that is combined with fuzzy logic in order to perform the estimation of EM prices. The proposed method is based on the application of a clustering methodology that groups historic energy contracts according to their prices’ similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts’ history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts.

  20. Study of decision framework of offshore wind power station site selection based on ELECTRE-III under intuitionistic fuzzy environment: A case of China

    International Nuclear Information System (INIS)

    Wu, Yunna; Zhang, Jinying; Yuan, Jianping; Geng, Shuai; Zhang, Haobo

    2016-01-01

    Highlights: • A novel MCDM framework is applied to assist group decision in OWPS site selection. • The index system consisting of veto and evaluation criteria is constructed. • A case study is carried five sites in coastal areas of Shandong in East China. - Abstract: Offshore wind power projects have been rapidly proposed in China due to policy promotion. Site selection immensely decides the success of any offshore wind power development and is a complex multi-criteria decision making (MCDM) problem. However, canonical MCDM methods tend to fail the site selection process due to the following three problems. Firstly, the compensation problem exists in information processing. Secondly, there exists the problem of incomplete utilization of decision information and information loss in the decision process. Thirdly, the interaction problem in the fuzzy environment is easy to be ignored. To deal with the above problems, this study builds a framework for offshore wind farm site selection decision utilizing Elimination et Choix Traduisant la Realité-III (ELECTRE-III) in the intuitionistic fuzzy environment. First of all, the comprehensive index system of OWPS site selection consisting of veto criteria and evaluation criteria is constructed. Then, the intuitionistic fuzzy set is used in the group decision for the decision makers to express the imperfect knowledge. Moreover, the generalized intuitionistic fuzzy ordered weighted geometric interaction averaging (GIFWGIA) operator is applied to deal with the interaction problem. Together with the likelihood-based valued comparisons, imprecise decision information is reasonably used and information loss problem is rationally avoided. Then a case of China is studied based on the proposed framework, demonstrating the site selection methodology valid and practical. This study implements evaluation method for offshore wind power site selection and also provides a theoretical basis for the development of offshore wind power

  1. Evaluating Emergency Response Solutions for Sustainable Community Development by Using Fuzzy Multi-Criteria Group Decision Making Approaches: IVDHF-TOPSIS and IVDHF-VIKOR

    Directory of Open Access Journals (Sweden)

    Junling Zhang

    2016-03-01

    Full Text Available Emergency management is vital in implementing sustainable community development, for which community planning must include emergency response solutions to potential natural and manmade hazards. To help maintain such solution repository, we investigate effective fuzzy multi-criteria group decision making (FMCGDM approaches for the complex problems of evaluating alternative emergency response solutions, where weights for decision makers and criteria are unknown due to problem complexity. We employ interval-valued dual hesitant fuzzy (IVDHF set to address decision hesitancy more effectively. Based on IVDHF assessments, we develop a deviation maximizing model to compute criteria weights and another compatibility maximizing model to calculate weights for decision makers. Then, two ideal-solution-based FMCGDM approaches are proposed: (i by introducing a synthesized IVDHF group decision matrix into TOPSIS, we develop an IVDHF-TOPSIS approach for fuzzy group settings; (ii when emphasizing both maximum group utility and minimum individual regret, we extend VIKOR to develop an IVDHF-VIKOR approach, where the derived decision makers’ weights are utilized to obtain group decision matrix and the determined criteria weights are integrated to reflect the relative importance of distances from the compromised ideal solution. Compared with aggregation-operators-based approach, IVDHF-TOPSIS and IVDHF-VIKOR can alleviate information loss and computational complexity. Numerical examples have validated the effectiveness of the proposed approaches.

  2. A Fuzzy-Grey Multicriteria Decision Making Approach for Green Supplier Selection in Low-Carbon Supply Chain

    Directory of Open Access Journals (Sweden)

    Qinghua Pang

    2017-01-01

    Full Text Available Due to the increasing awareness of global warming and environmental protection, many practitioners and researchers have paid much attention to the low-carbon supply chain management in recent years. Green supplier selection is one of the most critical activities in the low-carbon supply chain management, so it is important to establish the comprehensive criteria and develop a method for green supplier selection in low-carbon supply chain. The paper proposes a fuzz-grey multicriteria decision making approach to deal with these problems. First, the paper establishes 4 main criteria and 22 subcriteria for green supplier selection. Then, a method integrating fuzzy set theory and grey relational analysis is proposed. It uses the membership function of normal distribution to compare each supplier and uses grey relation analysis to calculate the weight of each criterion and improves fuzzy comprehensive evaluation. The proposed method can make the localization of individual green supplier more objectively and more accurately in the same trade. Finally, a case study in the steel industry is presented to demonstrate the effectiveness of the proposed approach.

  3. Post-fall decision tree development and implementation.

    Science.gov (United States)

    Gordon, Bonita M; Wnek, Theresa Frissora; Glorius, Nancy; Hasdorff, Carmen; Shiverski, Joyce; Ginn, Janet

    2010-01-01

    Care and evaluation after a patient's fall require a number of steps to ensure that appropriate care is given and injury is minimized. Astute and appropriate assessment skills with strategic interventions and communication can minimize the harm from a fall. Post-Fall Decision Guidelines were developed to guide care and treatment and to identify potential complications after a patient has fallen. This systematic approach mobilizes the steps of communication, using the Situation-Background-Assessment-Recommendation (SBAR) format, and guides assessment interventions.

  4. Decision tree analysis to evaluate dry cow strategies under UK conditions

    OpenAIRE

    Berry, E.A.; Hogeveen, H.; Hillerton, J.E.

    2005-01-01

    Economic decisions on animal health strategies address the cost-benefit aspect along with animal welfare and public health concerns. Decision tree analysis at an individual cow level highlighted that there is little economic difference between the use of either dry cow antibiotic or an internal teat sealant in preventing a new intramammary infection in a cow free of infection in all quarters of the mammary gland at drying off. However, a potential net loss of over pound20 per cow might occur ...

  5. Prediction of the compression ratio for municipal solid waste using decision tree.

    Science.gov (United States)

    Heshmati R, Ali Akbar; Mokhtari, Maryam; Shakiba Rad, Saeed

    2014-01-01

    The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.

  6. Fuzzy multi-objective approach for optimal selection of suppliers and transportation decisions in an eco-efficient closed loop supply chain network

    DEFF Research Database (Denmark)

    Govindan, Kannan; Darbari, Jyoti Dhingra; Agarwal, Vernika

    2017-01-01

    into the decision making process by selecting environmentally responsible suppliers to procure components based on sustainable criteria, choosing appropriate recovery options for end-of-use (EOU) inkjet printers, and planning an efficient transportation network design for reducing the carbon emission...... activities. A weighted fuzzy mathematical programming approach is utilised for generating a fuzzy, properly efficient solution as the desired compromised solution for the CLSC network problem configuration. The relevance of the model is justified using a real data set derived from a case study of the firm...... with higher sustainable performance and vehicles with lesser emission rate could substantially enhance firm's sustainable image and result in higher profits in the future....

  7. Which Types of Leadership Styles Do Followers Prefer? A Decision Tree Approach

    Science.gov (United States)

    Salehzadeh, Reza

    2017-01-01

    Purpose: The purpose of this paper is to propose a new method to find the appropriate leadership styles based on the followers' preferences using the decision tree technique. Design/methodology/approach: Statistical population includes the students of the University of Isfahan. In total, 750 questionnaires were distributed; out of which, 680…

  8. Oblique decision trees using embedded support vector machines in classifier ensembles

    NARCIS (Netherlands)

    Menkovski, V.; Christou, I.; Efremidis, S.

    2008-01-01

    Classifier ensembles have emerged in recent years as a promising research area for boosting pattern recognition systems' performance. We present a new base classifier that utilizes oblique decision tree technology based on support vector machines for the construction of oblique (non-axis parallel)

  9. A decision tree approach using silvics to guide planning for forest restoration

    Science.gov (United States)

    Sharon M. Hermann; John S. Kush; John C. Gilbert

    2013-01-01

    We created a decision tree based on silvics of longleaf pine (Pinus palustris) and historical descriptions to develop approaches for restoration management at Horseshoe Bend National Military Park located in central Alabama. A National Park Service goal is to promote structure and composition of a forest that likely surrounded the 1814 battlefield....

  10. Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury.

    Science.gov (United States)

    Phan, Thanh G; Chen, Jian; Singhal, Shaloo; Ma, Henry; Clissold, Benjamin B; Ly, John; Beare, Richard

    2018-01-01

    Prognostication following hypoxic ischemic encephalopathy (brain injury) is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data. The inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003-2011). Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS), features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume) associates of severe disability and death. We used the area under the ROC (auROC) to determine accuracy of model. There were 41 (63.7% males) patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82-1.00). At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86-1.00). Our findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

  11. Development of a diagnostic decision tree for obstructive pulmonary diseases based on real-life data

    NARCIS (Netherlands)

    Metting, Esther I; In 't Veen, Johannes C C M; Dekhuijzen, P N Richard; van Heijst, Ellen; Kocks, Janwillem W H; Muilwijk-Kroes, Jacqueline B; Chavannes, Niels H; van der Molen, Thys

    2016-01-01

    The aim of this study was to develop and explore the diagnostic accuracy of a decision tree derived from a large real-life primary care population. Data from 9297 primary care patients (45% male, mean age 53±17 years) with suspicion of an obstructive pulmonary disease was derived from an

  12. Dynamic Security Assessment of Danish Power System Based on Decision Trees: Today and Tomorrow

    DEFF Research Database (Denmark)

    Rather, Zakir Hussain; Liu, Leo; Chen, Zhe

    2013-01-01

    The research work presented in this paper analyzes the impact of wind energy, phasing out of central power plants and cross border power exchange on dynamic security of Danish Power System. Contingency based decision tree (DT) approach is used to assess the dynamic security of present and future...

  13. Test Reviews: Euler, B. L. (2007). "Emotional Disturbance Decision Tree". Lutz, FL: Psychological Assessment Resources

    Science.gov (United States)

    Tansy, Michael

    2009-01-01

    The Emotional Disturbance Decision Tree (EDDT) is a teacher-completed norm-referenced rating scale published by Psychological Assessment Resources, Inc., in Lutz, Florida. The 156-item EDDT was developed for use as part of a broader assessment process to screen and assist in the identification of 5- to 18-year-old children for the special…

  14. A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence

    Science.gov (United States)

    Rey S. Ofren; Edward Harvey

    2000-01-01

    A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...

  15. What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis

    Science.gov (United States)

    Thomas, Emily H.; Galambos, Nora

    2004-01-01

    To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…

  16. The Americans with Disabilities Act: A Decision Tree for Social Services Administrators

    Science.gov (United States)

    O'Brien, Gerald V.; Ellegood, Christina

    2005-01-01

    The 1990 Americans with Disabilities Act has had a profound influence on social workers and social services administrators in virtually all work settings. Because of the multiple elements of the act, however, assessing the validity of claims can be a somewhat arduous and complicated task. This article provides a "decision tree" for…

  17. Relationships between average depth and number of misclassifications for decision trees

    KAUST Repository

    Chikalov, Igor

    2014-02-14

    This paper presents a new tool for the study of relationships between the total path length or the average depth and the number of misclassifications for decision trees. In addition to algorithm, the paper also presents the results of experiments with datasets from UCI ML Repository [9] and datasets representing Boolean functions with 10 variables.

  18. Relationships Between Average Depth and Number of Nodes for Decision Trees

    KAUST Repository

    Chikalov, Igor

    2013-07-24

    This paper presents a new tool for the study of relationships between total path length or average depth and number of nodes of decision trees. In addition to algorithm, the paper also presents the results of experiments with datasets from UCI ML Repository [1]. © Springer-Verlag Berlin Heidelberg 2014.

  19. Relationships Between Average Depth and Number of Nodes for Decision Trees

    KAUST Repository

    Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2013-01-01

    This paper presents a new tool for the study of relationships between total path length or average depth and number of nodes of decision trees. In addition to algorithm, the paper also presents the results of experiments with datasets from UCI ML

  20. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

    KAUST Repository

    Busbait, Monther I.

    2014-01-01

    We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum

  1. Relationships between average depth and number of misclassifications for decision trees

    KAUST Repository

    Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2014-01-01

    This paper presents a new tool for the study of relationships between the total path length or the average depth and the number of misclassifications for decision trees. In addition to algorithm, the paper also presents the results of experiments with datasets from UCI ML Repository [9] and datasets representing Boolean functions with 10 variables.

  2. An ordering heuristic for building Binary Decision Diagrams for fault-trees

    Energy Technology Data Exchange (ETDEWEB)

    Bouissou, M. [Electricite de France (EDF), 75 - Paris (France)

    1997-12-31

    Binary Decision Diagrams (BDD) have recently made a noticeable entry in the RAMS field. This kind of representation for boolean functions makes possible the assessment of complex fault-trees, both qualitatively (minimal cut-sets search) and quantitatively (exact calculation of top event probability). The object of the paper is to present a pre-processing of the fault-tree which ensures that the results given by different heuristics on the `optimized` fault-tree are not too sensitive to the way the tree is written. This property is based on a theoretical proof. In contrast with some well known heuristics, the method proposed is not based only on intuition and practical experiments. (author) 12 refs.

  3. An ordering heuristic for building Binary Decision Diagrams for fault-trees

    International Nuclear Information System (INIS)

    Bouissou, M.

    1997-01-01

    Binary Decision Diagrams (BDD) have recently made a noticeable entry in the RAMS field. This kind of representation for boolean functions makes possible the assessment of complex fault-trees, both qualitatively (minimal cut-sets search) and quantitatively (exact calculation of top event probability). The object of the paper is to present a pre-processing of the fault-tree which ensures that the results given by different heuristics on the 'optimized' fault-tree are not too sensitive to the way the tree is written. This property is based on a theoretical proof. In contrast with some well known heuristics, the method proposed is not based only on intuition and practical experiments. (author)

  4. The Multi-Attribute Group Decision-Making Method Based on Interval Grey Trapezoid Fuzzy Linguistic Variables

    Directory of Open Access Journals (Sweden)

    Kedong Yin

    2017-12-01

    Full Text Available With respect to multi-attribute group decision-making (MAGDM problems, where attribute values take the form of interval grey trapezoid fuzzy linguistic variables (IGTFLVs and the weights (including expert and attribute weight are unknown, improved grey relational MAGDM methods are proposed. First, the concept of IGTFLV, the operational rules, the distance between IGTFLVs, and the projection formula between the two IGTFLV vectors are defined. Second, the expert weights are determined by using the maximum proximity method based on the projection values between the IGTFLV vectors. The attribute weights are determined by the maximum deviation method and the priorities of alternatives are determined by improved grey relational analysis. Finally, an example is given to prove the effectiveness of the proposed method and the flexibility of IGTFLV.

  5. The Multi-Attribute Group Decision-Making Method Based on Interval Grey Trapezoid Fuzzy Linguistic Variables.

    Science.gov (United States)

    Yin, Kedong; Wang, Pengyu; Li, Xuemei

    2017-12-13

    With respect to multi-attribute group decision-making (MAGDM) problems, where attribute values take the form of interval grey trapezoid fuzzy linguistic variables (IGTFLVs) and the weights (including expert and attribute weight) are unknown, improved grey relational MAGDM methods are proposed. First, the concept of IGTFLV, the operational rules, the distance between IGTFLVs, and the projection formula between the two IGTFLV vectors are defined. Second, the expert weights are determined by using the maximum proximity method based on the projection values between the IGTFLV vectors. The attribute weights are determined by the maximum deviation method and the priorities of alternatives are determined by improved grey relational analysis. Finally, an example is given to prove the effectiveness of the proposed method and the flexibility of IGTFLV.

  6. Multivariate decision tree designing for the classification of multi-jet topologies in e sup + e sup - collisions

    CERN Document Server

    Mjahed, M

    2002-01-01

    The binary decision tree method is used to separate between several multi-jet topologies in e sup + e sup - collisions. Instead of the univariate process usually taken, a new design procedure for constructing multivariate decision trees is proposed. The segmentation is obtained by considering some features functions, where linear and non-linear discriminant functions and a minimal distance method are used. The classification focuses on ALEPH simulated events, with multi-jet topologies. Compared to a standard univariate tree, the multivariate decision trees offer significantly better performance.

  7. Ultrasonographic diagnosis of biliary atresia based on a decision-making tree model

    International Nuclear Information System (INIS)

    Lee, So Mi; Cheon, Jung Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun Hye; Kim, In One; You, Sun Kyoung

    2015-01-01

    To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology

  8. Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model.

    Science.gov (United States)

    Lee, So Mi; Cheon, Jung-Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hae; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung

    2015-01-01

    To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology.

  9. Ultrasonographic diagnosis of biliary atresia based on a decision-making tree model

    Energy Technology Data Exchange (ETDEWEB)

    Lee, So Mi; Cheon, Jung Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun Hye; Kim, In One; You, Sun Kyoung [Dept. of Radiology, Seoul National University College of Medicine, Seoul (Korea, Republic of)

    2015-12-15

    To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology.

  10. Simple Prediction of Type 2 Diabetes Mellitus via Decision Tree Modeling

    Directory of Open Access Journals (Sweden)

    Mehrab Sayadi

    2017-06-01

    Full Text Available Background: Type 2 Diabetes Mellitus (T2DM is one of the most important risk factors in cardiovascular disorders considered as a common clinical and public health problem. Early diagnosis can reduce the burden of the disease. Decision tree, as an advanced data mining method, can be used as a reliable tool to predict T2DM. Objectives: This study aimed to present a simple model for predicting T2DM using decision tree modeling. Materials and Methods: This analytical model-based study used a part of the cohort data obtained from a database in Healthy Heart House of Shiraz, Iran. The data included routine information, such as age, gender, Body Mass Index (BMI, family history of diabetes, and systolic and diastolic blood pressure, which were obtained from the individuals referred for gathering baseline data in Shiraz cohort study from 2014 to 2015. Diabetes diagnosis was used as binary datum. Decision tree technique and J48 algorithm were applied using the WEKA software (version 3.7.5, New Zealand. Additionally, Receiver Operator Characteristic (ROC curve and Area Under Curve (AUC were used for checking the goodness of fit. Results: The age of the 11302 cases obtained after data preparation ranged from 18 to 89 years with the mean age of 48.1 ± 11.4 years. Additionally, 51.1% of the cases were male. In the tree structure, blood pressure and age were placed where most information was gained. In our model, however, gender was not important and was placed on the final branch of the tree. Total precision and AUC were 87% and 89%, respectively. This indicated that the model had good accuracy for distinguishing patients from normal individuals. Conclusions: The results showed that T2DM could be predicted via decision tree model without laboratory tests. Thus, this model can be used in pre-clinical and public health screening programs.

  11. Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles

    Directory of Open Access Journals (Sweden)

    Wong G William

    2008-06-01

    Full Text Available Abstract Background Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detection of this cancer type is urgently needed. In recent years, proteomics profiling techniques combined with various data analysis methods have been successfully used to gain critical insights into processes and mechanisms underlying pathologic conditions, particularly as they relate to cancer. However, the high dimensionality of proteomics data combined with their relatively small sample sizes poses a significant challenge to current data mining methodology where many of the standard methods cannot be applied directly. Here, we propose a novel methodological framework using machine learning method, in which decision tree based classifier ensembles coupled with feature selection methods, is applied to proteomics data generated from premalignant pancreatic cancer. Results This study explores the utility of three different feature selection schemas (Student t test, Wilcoxon rank sum test and genetic algorithm to reduce the high dimensionality of a pancreatic cancer proteomic dataset. Using the top features selected from each method, we compared the prediction performances of a single decision tree algorithm C4.5 with six different decision-tree based classifier ensembles (Random forest, Stacked generalization, Bagging, Adaboost, Logitboost and Multiboost. We show that ensemble classifiers always outperform single decision tree classifier in having greater accuracies and smaller prediction errors when applied to a pancreatic cancer proteomics dataset. Conclusion In our cross validation framework, classifier ensembles generally have better classification accuracies compared to that of a single decision tree when applied to a pancreatic cancer proteomic dataset, thus suggesting its utility in future proteomics data analysis. Additionally, the use of feature selection

  12. The application of a decision tree to establish the parameters associated with hypertension.

    Science.gov (United States)

    Tayefi, Maryam; Esmaeili, Habibollah; Saberi Karimian, Maryam; Amirabadi Zadeh, Alireza; Ebrahimi, Mahmoud; Safarian, Mohammad; Nematy, Mohsen; Parizadeh, Seyed Mohammad Reza; Ferns, Gordon A; Ghayour-Mobarhan, Majid

    2017-02-01

    Hypertension is an important risk factor for cardiovascular disease (CVD). The goal of this study was to establish the factors associated with hypertension by using a decision-tree algorithm as a supervised classification method of data mining. Data from a cross-sectional study were used in this study. A total of 9078 subjects who met the inclusion criteria were recruited. 70% of these subjects (6358 cases) were randomly allocated to the training dataset for the constructing of the decision-tree. The remaining 30% (2720 cases) were used as the testing dataset to evaluate the performance of decision-tree. Two models were evaluated in this study. In model I, age, gender, body mass index, marital status, level of education, occupation status, depression and anxiety status, physical activity level, smoking status, LDL, TG, TC, FBG, uric acid and hs-CRP were considered as input variables and in model II, age, gender, WBC, RBC, HGB, HCT MCV, MCH, PLT, RDW and PDW were considered as input variables. The validation of the model was assessed by constructing a receiver operating characteristic (ROC) curve. The prevalence rates of hypertension were 32% in our population. For the decision-tree model I, the accuracy, sensitivity, specificity and area under the ROC curve (AUC) value for identifying the related risk factors of hypertension were 73%, 63%, 77% and 0.72, respectively. The corresponding values for model II were 70%, 61%, 74% and 0.68, respectively. We have developed a decision tree model to identify the risk factors associated with hypertension that maybe used to develop programs for hypertension management. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  13. Predicting the probability of mortality of gastric cancer patients using decision tree.

    Science.gov (United States)

    Mohammadzadeh, F; Noorkojuri, H; Pourhoseingholi, M A; Saadat, S; Baghestani, A R

    2015-06-01

    Gastric cancer is the fourth most common cancer worldwide. This reason motivated us to investigate and introduce gastric cancer risk factors utilizing statistical methods. The aim of this study was to identify the most important factors influencing the mortality of patients who suffer from gastric cancer disease and to introduce a classification approach according to decision tree model for predicting the probability of mortality from this disease. Data on 216 patients with gastric cancer, who were registered in Taleghani hospital in Tehran,Iran, were analyzed. At first, patients were divided into two groups: the dead and alive. Then, to fit decision tree model to our data, we randomly selected 20% of dataset to the test sample and remaining dataset considered as the training sample. Finally, the validity of the model examined with sensitivity, specificity, diagnosis accuracy and the area under the receiver operating characteristic curve. The CART version 6.0 and SPSS version 19.0 softwares were used for the analysis of the data. Diabetes, ethnicity, tobacco, tumor size, surgery, pathologic stage, age at diagnosis, exposure to chemical weapons and alcohol consumption were determined as effective factors on mortality of gastric cancer. The sensitivity, specificity and accuracy of decision tree were 0.72, 0.75 and 0.74 respectively. The indices of sensitivity, specificity and accuracy represented that the decision tree model has acceptable accuracy to prediction the probability of mortality in gastric cancer patients. So a simple decision tree consisted of factors affecting on mortality of gastric cancer may help clinicians as a reliable and practical tool to predict the probability of mortality in these patients.

  14. Predicting metabolic syndrome using decision tree and support vector machine methods

    Directory of Open Access Journals (Sweden)

    Farzaneh Karimi-Alavijeh

    2016-06-01

    Full Text Available BACKGROUND: Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. METHODS: This study aims to employ decision tree and support vector machine (SVM to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP, diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs, total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. RESULTS: SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758, 0.74 (0.72 and 0.757 (0.739 in SVM (decision tree method. CONCLUSION: The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most

  15. Predicting metabolic syndrome using decision tree and support vector machine methods.

    Science.gov (United States)

    Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

    2016-05-01

    Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According

  16. Klasifikasi Nilai Kelayakan Calon Debitur Baru Menggunakan Decision Tree C4.5

    Directory of Open Access Journals (Sweden)

    Bambang Hermanto

    2017-01-01

    Full Text Available In an effort to improve the quality of customer service, especially in terms of feasibility assessment of borrowers due to the increasing number of new prospective borrowers loans financing the purchase of a motor vehicle, then the company needs a decision making tool allowing you to easily and quickly estimate Where the debtor is able to pay off the loans. This study discusses the process generates C4.5 decision tree algorithm and utilizing the learning group of debtor financing dataset motorcycle. The decision tree is then interpreted into the form of decision rules that can be understood and used as a reference in processing the data of borrowers in determining the feasibility of prospective new borrowers. Feasibility value refers to the value of the destination parameter credit status. If the value of the credit is paid off status mean estimated prospective borrower is able to repay the loan in question, but if the credit status parameters estimated worth pull means candidates concerned debtor is unable to pay loans.. System testing is done by comparing the results of the testing data by learning data in three scenarios with the decision that the data is valid at over 70% for all case scenarios. Moreover, in generated tree  and generate rules takes fairly quickly, which is no more than 15 minutes for each test scenario

  17. Prognostic Factors and Decision Tree for Long-term Survival in Metastatic Uveal Melanoma.

    Science.gov (United States)

    Lorenzo, Daniel; Ochoa, María; Piulats, Josep Maria; Gutiérrez, Cristina; Arias, Luis; Català, Jaum; Grau, María; Peñafiel, Judith; Cobos, Estefanía; Garcia-Bru, Pere; Rubio, Marcos Javier; Padrón-Pérez, Noel; Dias, Bruno; Pera, Joan; Caminal, Josep Maria

    2017-12-04

    The purpose of this study was to demonstrate the existence of a bimodal survival pattern in metastatic uveal melanoma. Secondary aims were to identify the characteristics and prognostic factors associated with long-term survival and to develop a clinical decision tree. The medical records of 99 metastatic uveal melanoma patients were retrospectively reviewed. Patients were classified as either short (≤ 12 months) or long-term survivors (> 12 months) based on a graphical interpretation of the survival curve after diagnosis of the first metastatic lesion. Ophthalmic and oncological characteristics were assessed in both groups. Of the 99 patients, 62 (62.6%) were classified as short-term survivors, and 37 (37.4%) as long-term survivors. The multivariate analysis identified the following predictors of long-term survival: age ≤ 65 years (p=0.012) and unaltered serum lactate dehydrogenase levels (p=0.018); additionally, the size (smaller vs. larger) of the largest liver metastasis showed a trend towards significance (p=0.063). Based on the variables significantly associated with long-term survival, we developed a decision tree to facilitate clinical decision-making. The findings of this study demonstrate the existence of a bimodal survival pattern in patients with metastatic uveal melanoma. The presence of certain clinical characteristics at diagnosis of distant disease is associated with long-term survival. A decision tree was developed to facilitate clinical decision-making and to counsel patients about the expected course of disease.

  18. Closeness Degree-Based Hesitant Trapezoidal Fuzzy Multicriteria Decision Making Method for Evaluating Green Suppliers with Qualitative Information

    Directory of Open Access Journals (Sweden)

    Xiaolu Zhang

    2018-01-01

    Full Text Available The aim of this study is to develop a new closeness degree-based hesitant trapezoidal fuzzy (HTrF multicriteria decision making (MCDM approach for identifying the most appropriate green suppliers in food supply chain involving uncertain qualitative evaluation information. The uniqueness of the proposed HTrF MCDM method is the consideration of uncertain qualitative information represented by flexible linguistic expressions based on HTrF values and the construction of compromise solution with the revised closeness degree. The revised closeness degree can make sure that the most appropriate solution has the shortest distance from the HTrF positive ideal solution and the farthest distance from the HTrF negative ideal solution, simultaneously. This proposed HTrF MCDM technique not only offers a simple and efficient decision support tool to aid the food firms for identifying the optimal suppliers in food supply chain but also can enable the managers of food firms to better understand the complete evaluation and decision processes. In addition, this study provides a novel defuzzification technique to manage the HTrF weights values of main-criteria and subcriteria, respectively.

  19. Assessment of health-care waste disposal methods using a VIKOR-based fuzzy multi-criteria decision making method.

    Science.gov (United States)

    Liu, Hu-Chen; Wu, Jing; Li, Ping

    2013-12-01

    Nowadays selection of the appropriate treatment method in health-care waste (HCW) management has become a challenge task for the municipal authorities especially in developing countries. Assessment of HCW disposal alternatives can be regarded as a complicated multi-criteria decision making (MCDM) problem which requires consideration of multiple alternative solutions and conflicting tangible and intangible criteria. The objective of this paper is to present a new MCDM technique based on fuzzy set theory and VIKOR method for evaluating HCW disposal methods. Linguistic variables are used by decision makers to assess the ratings and weights for the established criteria. The ordered weighted averaging (OWA) operator is utilized to aggregate individual opinions of decision makers into a group assessment. The computational procedure of the proposed framework is illustrated through a case study in Shanghai, one of the largest cities of China. The HCW treatment alternatives considered in this study include "incineration", "steam sterilization", "microwave" and "landfill". The results obtained using the proposed approach are analyzed in a comparative way. Copyright © 2013. Published by Elsevier Ltd.

  20. Multi-stage ranking of emergency technology alternatives for water source pollution accidents using a fuzzy group decision making tool.

    Science.gov (United States)

    Qu, Jianhua; Meng, Xianlin; You, Hong

    2016-06-05

    Due to the increasing number of unexpected water source pollution events, selection of the most appropriate disposal technology for a specific pollution scenario is of crucial importance to the security of urban water supplies. However, the formulation of the optimum option is considerably difficult owing to the substantial uncertainty of such accidents. In this research, a multi-stage technical screening and evaluation tool is proposed to determine the optimal technique scheme, considering the areas of pollutant elimination both in drinking water sources and water treatment plants. In stage 1, a CBR-based group decision tool was developed to screen available technologies for different scenarios. Then, the threat degree caused by the pollution was estimated in stage 2 using a threat evaluation system and was partitioned into four levels. For each threat level, a corresponding set of technique evaluation criteria weights was obtained using Group-G1. To identify the optimization alternatives corresponding to the different threat levels, an extension of TOPSIS, a multi-criteria interval-valued trapezoidal fuzzy decision making technique containing the four arrays of criteria weights, to a group decision environment was investigated in stage 3. The effectiveness of the developed tool was elaborated by two actual thallium-contaminated scenarios associated with different threat levels. Copyright © 2016 Elsevier B.V. All rights reserved.