Multi-objective evolutionary granular rule-based classifiers: An experimental comparison

In this paper, we analyze and compare four multi-objective evolutionary granular rule-based classifiers. We learn concurrently the rule base, the most suitable number of granules and their parameters during the evolutionary process. Rule learning is performed by a method, which selects rules and conditions from an initial heuristically-generated rule base. The four classifiers differ for the type of granule, namely Type-1 and Type-2 fuzzy sets, and for the method used for generating the initial rule base, namely crisp and fuzzy decision tree learning algorithms. Results show that generating the initial rule base by using a fuzzy decision tree outperforms the use of the crisp decision tree. On the other hand, no statistical difference exists between the use of Type-1 and Type-2 fuzzy sets as granules.

[1]  Oscar Castillo,et al.  A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition , 2014, Appl. Soft Comput..

[2]  Didier Dubois,et al.  Bridging gaps between several forms of granular computing , 2016, Granular Computing.

[3]  M. Friedman.  The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[4]  Beatrice Lazzerini,et al.  Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework , 2009, Int. J. Approx. Reason..

[5]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[6]  Francisco Herrera,et al.  Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures , 2011, Inf. Sci..

[7]  W. Pedrycz,et al.  Granular computing and intelligent systems : design with information granules of higher order and higher type , 2011 .

[8]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[9]  Witold Pedrycz,et al.  Granular Computing: Analysis and Design of Intelligent Systems , 2013 .

[10]  Witold Pedrycz,et al.  On Distributed Fuzzy Decision Trees for Big Data , 2018, IEEE Transactions on Fuzzy Systems.

[11]  María José del Jesús,et al.  Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges , 2015, Knowl. Based Syst..

[12]  Qinghua Hu,et al.  Granular Computing Based Machine Learning in the Era of Big Data , 2017, Inf. Sci..

[13]  R. Iman,et al.  Approximations of the critical region of the fbietkan statistic , 1980 .

[14]  Witold Pedrycz,et al.  Granular Computing: Perspectives and Challenges , 2013, IEEE Transactions on Cybernetics.

[15]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[16]  Beatrice Lazzerini,et al.  Multi-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems , 2009, Evol. Intell..

[17]  Francisco Herrera,et al.  A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions , 2013, IEEE Transactions on Fuzzy Systems.

[18]  P. Ducange,et al.  Multi-objective evolutionary design of granular rule-based classifiers , 2016 .

[19]  Michela Antonelli,et al.  A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers , 2014, Inf. Sci..