Complexity, interpretability and explanation capability of fuzzy rule-based classifiers
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[1] Anna Maria Fanelli,et al. Interpretability constraints for fuzzy information granulation , 2008, Inf. Sci..
[2] Hisao Ishibuchi,et al. Multiobjective Genetic Fuzzy Systems: Review and Future Research Directions , 2007, 2007 IEEE International Fuzzy Systems Conference.
[3] Hisao Ishibuchi,et al. Effect of rule weights in fuzzy rule-based classification systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).
[4] Hisao Ishibuchi,et al. Designing fuzzy rule-based classifiers that can visually explain their classification results to human users , 2008, 2008 3rd International Workshop on Genetic and Evolving Systems.
[5] Hannu Koivisto,et al. Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms , 2008, Int. J. Approx. Reason..
[6] Francisco Herrera,et al. Genetic fuzzy systems: taxonomy, current research trends and prospects , 2008, Evol. Intell..
[7] Antonio González Muñoz,et al. Including a simplicity criterion in the selection of the best rule in a genetic fuzzy learning algorithm , 2001, Fuzzy Sets Syst..
[8] Kim-Fung Man,et al. Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction , 2005, Fuzzy Sets Syst..
[9] Francisco Herrera,et al. Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..
[10] Gary B. Lamont,et al. Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.
[11] Kim-Fung Man,et al. Agent-based evolutionary approach for interpretable rule-based knowledge extraction , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[12] Alessio Botta,et al. Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index , 2008, Soft Comput..
[13] Hisao Ishibuchi,et al. Selecting fuzzy if-then rules for classification problems using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..
[14] H. Ishibuchi,et al. A visual explanation system for explaining fuzzy reasoning results by fuzzy rule-based classifiers , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.
[15] Beatrice Lazzerini,et al. A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems , 2007, Soft Comput..
[16] Gary B. Lamont,et al. Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .
[17] Carmen Lacave,et al. A review of explanation methods for heuristic expert systems , 2004, The Knowledge Engineering Review.
[18] Hisao Ishibuchi,et al. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining , 2004, Fuzzy Sets Syst..
[19] Yaochu Jin,et al. Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement , 2000, IEEE Trans. Fuzzy Syst..
[20] Tong Heng Lee,et al. Multiobjective Evolutionary Algorithms and Applications , 2005, Advanced Information and Knowledge Processing.
[21] Magne Setnes,et al. GA-fuzzy modeling and classification: complexity and performance , 2000, IEEE Trans. Fuzzy Syst..
[22] Serge Guillaume,et al. Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..
[23] Hisao Ishibuchi,et al. Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems , 1997, Fuzzy Sets Syst..
[24] Satchidananda Dehuri,et al. Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases , 2008, Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases.
[25] Hisao Ishibuchi,et al. Discussions on Interpretability of Fuzzy Systems using Simple Examples , 2009, IFSA/EUSFLAT Conf..
[26] Rafael Muñoz-Salinas,et al. Automatic Tuning of a Fuzzy Visual System Using Evolutionary Algorithms: Single-Objective Versus Multiobjective Approaches , 2008, IEEE Transactions on Fuzzy Systems.
[27] Yaochu Jin,et al. Multi-Objective Machine Learning , 2006, Studies in Computational Intelligence.
[28] Marco Cococcioni. The Evolutionary Multiobjective Optimization of Fuzzy Rule-Based Systems Bibliography Page , 2009 .
[29] Hisao Ishibuchi,et al. Rule weight specification in fuzzy rule-based classification systems , 2005, IEEE Transactions on Fuzzy Systems.
[30] Hisao Ishibuchi,et al. Three-objective genetics-based machine learning for linguistic rule extraction , 2001, Inf. Sci..
[31] Ralf Mikut,et al. Interpretability issues in data-based learning of fuzzy systems , 2005, Fuzzy Sets Syst..
[32] Francisco Herrera,et al. Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems , 2008, Soft Comput..
[33] Francisco Herrera,et al. A Multi-Objective Genetic Algorithm for Tuning and Rule Selection to Obtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems , 2007, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[34] David McSherry,et al. Introduction to the Special Issue on Explanation in Case-Based Reasoning , 2005, Artificial Intelligence Review.
[35] María José del Jesús,et al. Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction , 2005, IEEE Transactions on Fuzzy Systems.
[36] Gary B. Lamont,et al. Applications Of Multi-Objective Evolutionary Algorithms , 2004 .
[37] F. Herrera,et al. A proposal on reasoning methods in fuzzy rule-based classification systems , 1999 .
[38] Francisco Herrera,et al. Genetic Fuzzy Systems: Status, Critical Considerations and Future Directions , 2005 .
[39] Bernhard Sendhoff,et al. On generating FC3 fuzzy rule systems from data using evolution strategies , 1999, IEEE Trans. Syst. Man Cybern. Part B.
[40] Hisao Ishibuchi,et al. Selecting linguistic classification rules by two-objective genetic algorithms , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.
[41] Ferenc Szeifert,et al. Data-driven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization , 2003, Int. J. Approx. Reason..
[42] John Q. Gan,et al. Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling , 2008, Fuzzy Sets Syst..
[43] Francisco Herrera,et al. Linguistic modeling with hierarchical systems of weighted linguistic rules , 2003, Int. J. Approx. Reason..
[44] R. K. Ursem. Multi-objective Optimization using Evolutionary Algorithms , 2009 .