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

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.