Learning interpretable fuzzy inference systems with FisPro

Fuzzy inference systems (FIS) are likely to play a significant part in system modeling, provided that they remain interpretable following learning from data. The aim of this paper is to set up some guidelines for interpretable FIS learning, based on practical experience with fuzzy modeling in various fields. An open source software system called FisPro has been specifically designed to provide generic tools for interpretable FIS design and learning. It can then be extended with the addition of new contributions. This work presents a global approach to design data-driven FIS that satisfy certain interpretability and accuracy criteria. It includes fuzzy partition generation, rule learning, input space reduction and rule base simplification. The FisPro implementation is discussed and illustrated through several detailed case studies.

[1]  Terry Bossomaier,et al.  Simulation of trust in client- wealth management advisor relationships , 2008, ANSS 2008.

[2]  P.-Y. Glorennec.  Quelques aspects analytiques des systèmes d'inférence floue , 1996 .

[3]  Piedad Brox Jiménez,et al.  Using Xfuzzy Environment for the Whole Design of Fuzzy Systems , 2007, 2007 IEEE International Fuzzy Systems Conference.

[4]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[5]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[6]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[7]  SergeGuillaume,et al.  Fuzzy Models to Deal with Sensory Data in Food Industry , 2004 .

[8]  Andri Riid,et al.  Transparent Fuzzy Systems in Modelling and Control , 2003 .

[9]  J. Casillas.  Interpretability issues in fuzzy modeling , 2003 .

[10]  Detlef D. Nauck.  GNU Fuzzy , 2007, 2007 IEEE International Fuzzy Systems Conference.

[11]  Sébastien Destercke,et al.  Building an interpretable fuzzy rule base from data using Orthogonal Least Squares - Application to a depollution problem , 2007, Fuzzy Sets Syst..

[12]  Serge Guillaume,et al.  Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..

[13]  Eyke Hüllermeier,et al.  Why Fuzzy Decision Trees are Good Rankers , 2009, IEEE Transactions on Fuzzy Systems.

[14]  M. MendelJ.,et al.  Fuzzy basis functions , 1995 .

[15]  Brigitte Charnomordic,et al.  Fuzzy Inference Systems to Model Sensory Evaluation , 2004 .

[16]  Francisco Herrera,et al.  Integration of an Index to Preserve the Semantic Interpretability in the Multiobjective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems , 2010, IEEE Transactions on Fuzzy Systems.

[17]  Jose M. Alonso,et al.  GUAJE, A Java environment for generating understandable and accurate models , 2010 .

[18]  Min-You Chen.  Establishing interpretable fuzzy models from numeric data , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[19]  José M. Alonso,et al.  KBCT: a knowledge extraction and representation tool for fuzzy logic based systems , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[20]  Xianyi Zeng,et al.  Intelligent Sensory Evaluation: Methodologies and Applications , 2004 .

[21]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[22]  Richard Weber,et al.  Fuzzy-ID3: A class of methods for automatic knowledge acquisition , 1992 .

[23]  Serge Guillaume,et al.  Influence of micrometeorological factors on pesticide loss to the air during vine spraying : Data analysis with statistical and fuzzy inference models , 2008 .

[24]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[25]  S. Guillaume,et al.  Development and validation of fuzzy logic inference to determine optimum rates of N for corn on the basis of field and crop features , 2010, Precision Agriculture.

[26]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[27]  José M. Alonso,et al.  HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism , 2008, Int. J. Intell. Syst..

[28]  Didier Dubois,et al.  Practical Inference With Systems of Gradual Implicative Rules , 2009, IEEE Transactions on Fuzzy Systems.

[29]  János Abonyi,et al.  Learning fuzzy classification rules from labeled data , 2003, Inf. Sci..

[30]  Christian Borgelt,et al.  FrIDA -A Free Intelligent Data Analysis Toolbox , 2007, 2007 IEEE International Fuzzy Systems Conference.

[31]  José Valente de Oliveira,et al.  Semantic constraints for membership function optimization , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[32]  Luis Magdalena,et al.  Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview , 2003 .

[33]  Anna Maria Fanelli,et al.  Interpretability constraints for fuzzy information granulation , 2008, Inf. Sci..

[34]  Vincent Fromion,et al.  Simulation and optimization software for alcoholic fermentation in winemaking conditions. , 2009 .

[35]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[36]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[37]  Ronald R. Yager,et al.  A framework for reasoning with soft information , 2010, Inf. Sci..

[38]  T. Rajaram,et al.  Modeling of interactions among sustainability components of an agro-ecosystem using local knowledge through cognitive mapping and fuzzy inference system , 2010, Expert Syst. Appl..

[39]  A. Bégué,et al.  Integrating SPOT-5 time series, crop growth modeling and expert knowledge for monitoring agricultural practices — The case of sugarcane harvest on Reunion Island , 2009 .