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Subtractive Clustering and Particle Swarm Optimization Based Fuzzy Classifier

Author

Listed:
  • Halima Salah

    (LabSTIC laboratory, University 8 mai 1945 Guelma, Algeria)

  • Mohamed Nemissi

    (LabSTIC laboratory, University 8 mai 1945 Guelma, Algeria)

  • Hamid Seridi

    (LabSTIC laboratory, University 8 mai 1945 Guelma, Guelma, Algeria)

  • Herman Akdag

    (LIASD laboratory, University Paris 8, Paris, France)

Abstract

Setting a compact and accurate rule base constitutes the principal objective in designing fuzzy rule-based classifiers. In this regard, the authors propose a designing scheme based on the combination of the subtractive clustering (SC) and the particle swarm optimization (PSO). The main idea relies on the application of the SC on each class separately and with a different radius in order to generate regions that are more accurate, and to represent each region by a fuzzy rule. However, the number of rules is then affected by the radiuses, which are the main preset parameters of the SC. The PSO is therefore used to define the optimal radiuses. To get good compromise accuracy-compactness, the authors propose using a multi-objective function for the PSO. The performances of the proposed method are tested on well-known data sets and compared with several state-of-the-art methods.

Suggested Citation

  • Halima Salah & Mohamed Nemissi & Hamid Seridi & Herman Akdag, 2019. "Subtractive Clustering and Particle Swarm Optimization Based Fuzzy Classifier," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 8(3), pages 108-122, July.
    • Handle: RePEc:igg:jfsa00:v:8:y:2019:i:3:p:108-122
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