On the Use of Bagging, Mutual Information-Based Feature Selection and Multicriteria Genetic Algorithms to Design Fuzzy Rule-Based Classification Ensembles

Authors:
Oscar Cordón;Arnaud Quirin;Luciano Sánchez
Affiliations:
-;-;-
Venue:
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Year:
2008

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Comparing two genetic overproduce-and-choose strategies for fuzzy rule-based multiclassification systems generated by bagging and mutual information-based feature selection

International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models

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Abstract

In this contribution we explore the combination of bagging with random subspace and two variants of Battiti's mutual information feature selection methods to design fuzzy rule-based classification system ensembles. Besides, we consider a multicriteria genetic algorithm guided by the training error to select the component classifiers, in order to look for appropriate accuracy-complexity trade-offs in the final multiclassifier.