Special Issue: Neuro-Fuzzy Systems; Guest Editors: Leszek Rutkowski, M. J. Er and Janek Żurada — ArticlesNo Access

DESIGNING BOOSTING ENSEMBLE OF RELATIONAL FUZZY SYSTEMS

RAFAŁ SCHERER

Department of Computer Engineering, Czȩstochowa University of Technology, al. Armii Krajowej 36, 42-200 Czȩstochowa, Poland

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https://doi.org/10.1142/S0129065710002528Cited by:26 (Source: Crossref)

Abstract

A method frequently used in classification systems for improving classification accuracy is to combine outputs of several classifiers. Among various types of classifiers, fuzzy ones are tempting because of using intelligible fuzzy if-then rules. In the paper we build an AdaBoost ensemble of relational neuro-fuzzy classifiers. Relational fuzzy systems bond input and output fuzzy linguistic values by a binary relation; thus, fuzzy rules have additional, comparing to traditional fuzzy systems, weights — elements of a fuzzy relation matrix. Thanks to this the system is better adjustable to data during learning. In the paper an ensemble of relational fuzzy systems is proposed. The problem is that such an ensemble contains separate rule bases which cannot be directly merged. As systems are separate, we cannot treat fuzzy rules coming from different systems as rules from the same (single) system. In the paper, the problem is addressed by a novel design of fuzzy systems constituting the ensemble, resulting in normalization of individual rule bases during learning. The method described in the paper is tested on several known benchmarks and compared with other machine learning solutions from the literature.

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