A hybrid PSO-FSVM model and its application to imbalanced classification of mammograms

Authors:
Hussein Samma;Chee Peng Lim;Umi Kalthum Ngah
Affiliations:
Imaging & Computational Intelligence Group (ICI), School of Electrical and Electronic Engineering, University of Science Malaysia, Malaysia;Centre for Intelligent Systems Research, Deakin University, Australia;Imaging & Computational Intelligence Group (ICI), School of Electrical and Electronic Engineering, University of Science Malaysia, Malaysia
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Year:
2013

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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.