Designing ensembles of fuzzy classification systems: an immune-inspired approach

  • Authors:

  • Pablo D. Castro;Guilherme P. Coelho;Marcelo F. Caetano;Fernando J. Von Zuben

  • Affiliations:

  • Laboratory of Bioinformatics and Bioinspired Computing – LBiC, Department of Computer Engineering and Industrial Automation – DCA, School of Electrical and Computer Engineering – ...;Laboratory of Bioinformatics and Bioinspired Computing – LBiC, Department of Computer Engineering and Industrial Automation – DCA, School of Electrical and Computer Engineering – ...;Laboratory of Bioinformatics and Bioinspired Computing – LBiC, Department of Computer Engineering and Industrial Automation – DCA, School of Electrical and Computer Engineering – ...;Laboratory of Bioinformatics and Bioinspired Computing – LBiC, Department of Computer Engineering and Industrial Automation – DCA, School of Electrical and Computer Engineering – ...

  • Venue:
  • ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
  • Year:
  • 2005

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Abstract

In this work we propose an immune-based approach for designing of fuzzy systems. From numerical data and with membership function previously defined, the immune algorithm evolves a population of fuzzy classification rules based on the clonal selection, hypermutation and immune network principles. Once AIS are able to find multiple good solutions of the problem, accurate and diverse fuzzy systems are built in a single run. Hence, we construct an ensemble of these classifier in order to achieve better results. An ensemble of classifiers consists of a set of individual classifiers whose outputs are combined when classifying novel patterns. The good performance of an ensemble is strongly dependent of individual accuracy and diversity of its components. We evaluate the proposed methodology through computational experiments on some datasets. The results demonstrate that the performance of the obtained fuzzy systems in isolation is very good. However when we combine these systems, a significant improvement is obtained in the correct classification rate, outperforming the single best classifier.