GPF-CLASS: A Genetic Fuzzy model for classification

This work presents a Genetic Fuzzy Classification System (GFCS) called Genetic Programming Fuzzy Classification System (GPF-CLASS). This model differs from the traditional approach of GFCS, which uses the metaheuristic as a way to learn “if-then” fuzzy rules. This classical approach needs several changes and constraints on the use of genetic operators, evaluation and selection, which depends primarily on the metaheuristic used. Genetic Programming makes this implementation costly and explores few of its characteristics and potentialities. The GPF-CLASS model seeks for a greater integration with the metaheuristic: Multi-Gene Genetic Programming (MGGP), exploring its potential of terminals selection (input features) and functional form and at the same time aims to provide the user with a comprehension of the classification solution. Tests with 22 benchmarks datasets for classification have been performed and, as well as statistical analysis and comparisons with others Genetic Fuzzy Systems proposed in the literature.

[1]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[2]  Sean Luke,et al.  Lexicographic Parsimony Pressure , 2002, GECCO.

[3]  Hisao Ishibuchi,et al.  Effect of rule weights in fuzzy rule-based classification systems , 2001, IEEE Trans. Fuzzy Syst..

[4]  Shinn-Jang Ho,et al.  Design of accurate classifiers with a compact fuzzy-rule base using an evolutionary scatter partition of feature space , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[5]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[6]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[7]  Dominic P. Searson,et al.  GPTIPS: Genetic Programming and Symbolic Regression for Matlab , 2009 .

[8]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[9]  Witold Pedrycz,et al.  Fuzzy Systems Engineering , 2007 .

[10]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[11]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[12]  Robi Polikar,et al.  Incremental Learning of Concept Drift in Nonstationary Environments , 2011, IEEE Transactions on Neural Networks.

[13]  F. Herrera,et al.  Learning Fuzzy Rules using Genetic Programming : Context-free grammar definition for high-dimensionality problems , 2004 .

[14]  Antonio J. Rivera,et al.  GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems , 2010, Inf. Sci..

[15]  Hans-Jürgen Zimmermann,et al.  Fuzzy rule based classification with FeatureSelector and modified threshold accepting , 2000, Eur. J. Oper. Res..

[16]  Jorge Casillas,et al.  Fuzzy-XCS: A Michigan Genetic Fuzzy System , 2007, IEEE Transactions on Fuzzy Systems.

[17]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[18]  Inés Couso,et al.  Combining GP operators with SA search to evolve fuzzy rule based classifiers , 2001, Inf. Sci..

[19]  Dominic P. Searson,et al.  Co‐evolution of non‐linear PLS model components , 2007 .

[20]  Hisao Ishibuchi,et al.  Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[21]  Hisao Ishibuchi,et al.  Classification and modeling with linguistic information granules - advanced approaches to linguistic data mining , 2004, Advanced information processing.

[22]  J.Ma Troya Linero,et al.  Evolutionary design of fuzzy logic controllers using strongly-typed GP , 1999 .

[23]  Alireza Sadeghian,et al.  Derivation of relational fuzzy classification rules using evolutionary computation , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[24]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[25]  Riccardo Poli,et al.  Foundations of Genetic Programming , 1999, Springer Berlin Heidelberg.

[26]  María José del Jesús,et al.  KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..

[27]  Hisao Ishibuchi,et al.  Multiobjective Genetic Fuzzy Systems: Review and Future Research Directions , 2007, 2007 IEEE International Fuzzy Systems Conference.

[28]  Francisco Herrera,et al.  Genetic fuzzy systems: taxonomy, current research trends and prospects , 2008, Evol. Intell..

[29]  Athanasios Tsakonas,et al.  A comparison of classification accuracy of four genetic programming-evolved intelligent structures , 2006, Inf. Sci..

[30]  Shigeo Abe.  Support Vector Machines for Pattern Classification , 2010, Advances in Pattern Recognition.

[31]  Antonio González Muñoz,et al.  Table Ii Tc Pattern Recognition Result for 120 Eir Satellite Image Cases Selection of Relevant Features in a Fuzzy Genetic Learning Algorithm , 2001 .