Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning

Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions.

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

[2]  Hisao Ishibuchi,et al.  Evolutionary Multiobjective Optimization for Generating an Ensemble of Fuzzy Rule-Based Classifiers , 2003, GECCO.

[3]  Magne Setnes,et al.  Compact and transparent fuzzy models and classifiers through iterative complexity reduction , 2001, IEEE Trans. Fuzzy Syst..

[4]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

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

[6]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[7]  Hannu Koivisto,et al.  A Dynamically Constrained Multiobjective Genetic Fuzzy System for Regression Problems , 2010, IEEE Transactions on Fuzzy Systems.

[8]  Hisao Ishibuchi,et al.  Selection of Linguistic Classfication Rules Using Two-objective Genetic Algorithms , 1995 .

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

[10]  Francisco Herrera,et al.  A Multiobjective Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy-Rule-Based Systems , 2009, IEEE Transactions on Fuzzy Systems.

[11]  Hisao Ishibuchi,et al.  Optimization of Scalarizing Functions Through Evolutionary Multiobjective Optimization , 2007, EMO.

[12]  Hisao Ishibuchi,et al.  Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems , 1997, Fuzzy Sets Syst..

[13]  Héctor Pomares,et al.  Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation , 2003, IEEE Trans. Neural Networks.

[14]  Satchidananda Dehuri,et al.  Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases , 2008, Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases.

[15]  Beatrice Lazzerini,et al.  Evolutionary Multi-Objective Optimization of Fuzzy Rule-Based Classifiers in the ROC Space , 2007, 2007 IEEE International Fuzzy Systems Conference.

[16]  H. Ishibuchi,et al.  Distributed representation of fuzzy rules and its application to pattern classification , 1992 .

[17]  P. Fleming,et al.  Multi-objective genetic programming for nonlinear system identification , 1998 .

[18]  Carlos A. Coello Coello,et al.  Swarm Intelligence for Multi-objective Problems in Data Mining , 2009 .

[19]  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..

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

[21]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[22]  Yaochu Jin,et al.  Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement , 2000, IEEE Trans. Fuzzy Syst..

[23]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[24]  Beatrice Lazzerini,et al.  Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets , 2010, Soft Comput..

[25]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[26]  Hisao Ishibuchi,et al.  Incorporation of Scalarizing Fitness Functions into Evolutionary Multiobjective Optimization Algorithms , 2006, PPSN.

[27]  Andrzej Jaszkiewicz,et al.  On the computational efficiency of multiple objective metaheuristics. The knapsack problem case study , 2004, Eur. J. Oper. Res..

[28]  Peter J. Fleming,et al.  On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers , 1996, PPSN.

[29]  Hannu Koivisto,et al.  Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms , 2008, Int. J. Approx. Reason..

[30]  Bernhard Sendhoff,et al.  Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[31]  Christian Setzkorn,et al.  On the use of multi-objective evolutionary algorithms for the induction of fuzzy classification rule systems. , 2005, Bio Systems.

[32]  Carlos M. Fonseca,et al.  'Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[33]  Hussein A. Abbass,et al.  Speeding Up Backpropagation Using Multiobjective Evolutionary Algorithms , 2003, Neural Computation.

[34]  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..

[35]  E. Zio,et al.  Application of a niched Pareto genetic algorithm for selecting features for nuclear transients classification , 2009 .

[36]  Luiz Eduardo Soares de Oliveira,et al.  Multi-objective Genetic Algorithms to Create Ensemble of Classifiers , 2005, EMO.

[37]  Hisao Ishibuchi,et al.  An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization , 2008, Eur. J. Oper. Res..

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

[39]  F. Herrera,et al.  A proposal on reasoning methods in fuzzy rule-based classification systems , 1999 .

[40]  Tong Heng Lee,et al.  Multiobjective Evolutionary Algorithms and Applications , 2005, Advanced Information and Knowledge Processing.

[41]  Magne Setnes,et al.  GA-fuzzy modeling and classification: complexity and performance , 2000, IEEE Trans. Fuzzy Syst..

[42]  Francisco Herrera,et al.  Genetic Fuzzy Systems: Status, Critical Considerations and Future Directions , 2005 .

[43]  Mitsuo Gen,et al.  Specification of Genetic Search Directions in Cellular Multi-objective Genetic Algorithms , 2001, EMO.

[44]  Francisco Herrera,et al.  Other Genetic Fuzzy Rule-Based System Paradigms , 2001 .

[45]  Kiyoshi Tanaka,et al.  Controlling Dominance Area of Solutions and Its Impact on the Performance of MOEAs , 2007, EMO.

[46]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[47]  Andrzej Jaszkiewicz,et al.  On the performance of multiple-objective genetic local search on the 0/1 knapsack problem - a comparative experiment , 2002, IEEE Trans. Evol. Comput..

[48]  Hisao Ishibuchi,et al.  Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning , 2007, Int. J. Approx. Reason..

[49]  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.

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

[51]  Hisao Ishibuchi,et al.  Selecting fuzzy if-then rules for classification problems using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[52]  Yaochu Jin,et al.  Multi-Objective Machine Learning , 2006, Studies in Computational Intelligence.

[53]  Marco Cococcioni.  The Evolutionary Multiobjective Optimization of Fuzzy Rule-Based Systems Bibliography Page , 2009 .

[54]  Hisao Ishibuchi,et al.  Rule weight specification in fuzzy rule-based classification systems , 2005, IEEE Transactions on Fuzzy Systems.

[55]  José M. Alonso,et al.  A Conceptual Framework for Understanding a Fuzzy System , 2009, IFSA/EUSFLAT Conf..

[56]  Hisao Ishibuchi,et al.  Fuzzy data mining: effect of fuzzy discretization , 2001, Proceedings 2001 IEEE International Conference on Data Mining.