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    • Keywords


      Temporal fuzzy min–max (TFMM) neural network; particle swarm optimization algorithm (PSOA); pattern classification; rule extraction.

    • Abstract


      In this paper, we propose a new pattern classification system by combining Temporal features with Fuzzy Min–Max (TFMM) neural network based classifier for effective decision support in medical diagnosis. Moreover, a Particle Swarm Optimization (PSO) algorithm based rule extractor is also proposed in this work for improving the detection accuracy. Intelligent fuzzy rules are extracted from the temporal features with Fuzzy Min–Max neural network based classifier, and then PSO rule extractor is used to minimize the number of features in the extracted rules. We empirically evaluated the effectiveness of the proposed TFMM-PSO system using the UCI Machine Learning Repository Data Set. The results are analysed and compared with other published results. In addition, the detection accuracy is validated by using the ten-fold cross validation.

    • Author Affiliations


      S Ganapathy1 R Sethukkarasi1 P Yogesh1 P Vijayakumar2 A Kannan1

      1. Department of Information Science and Technology, Anna University, Chennai 600 025, India
      2. Department of Computer Science and Engineering, University College of Engineering, Tindivanam, Villupuram 604 001, India
    • Dates

  • Sadhana | News

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