Fuzzy Classification with Multi-objective Evolutionary Algorithms

Authors:
Fernando Jiménez;Gracia Sánchez;José F. Sánchez;José M. Alcaraz
Affiliations:
Facultad de Informática Campus de Espinardo, Murcia, Spain 30071;Facultad de Informática Campus de Espinardo, Murcia, Spain 30071;Facultad de Informática Campus de Espinardo, Murcia, Spain 30071;Facultad de Informática Campus de Espinardo, Murcia, Spain 30071
Venue:
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Year:
2008

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Abstract

In this work we propose, on the one hand, a multi-objective constrained optimization model to obtain fuzzy models for classification considering criteria of accuracy and interpretability. On the other hand, we propose an evolutionary multi-objective approach for fuzzy classification from data with real and discrete attributes. The multi-objective evolutionary approach has been evaluated by means of three different evolutionary schemes: Preselection with niches, NSGA-II and ENORA. The results have been compared in terms of effectiveness by means of statistical techniques using the well-known standard Iris data set.