A Hybrid PSO-FSVM Model and Its Application to Imbalanced Classification of Mammograms

  • Hussein Samma
  • Chee Peng Lim
  • Umi Kalthum Ngah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7802)

Abstract

In this work, a hybrid model comprising Particle Swarm Optimization (PSO) and the Fuzzy Support Vector Machine (FSVM) for tackling imbalanced classification problems is proposed. A PSO algorithm, guided by the G-mean measure, is used to optimize the FSVM parameters in imbalanced classification problems. The hybrid PSO-FSVM model is evaluated using a mammogram mass classification problem. The experimental results are analyzed and compared with those from other methods. The outcomes positively demonstrate that the proposed PSO-FSVM model is able to achieve comparable, if not better, results for imbalanced data classification problems.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hussein Samma
    • 1
  • Chee Peng Lim
    • 2
  • Umi Kalthum Ngah
    • 1
  1. 1.Imaging & Computational Intelligence Group (ICI), School of Electrical and Electronic EngineeringUniversity of Science MalaysiaMalaysia
  2. 2.Centre for Intelligent Systems ResearchDeakin UniversityAustralia

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