Augmenting cost-SVM with gaussian mixture models for imbalanced classification

Abstract

The Support Vector Machine (SVM), a known discriminative classifier is ineffective in dealing with imbalanced classificationproblems where the training examples of target class are outnumbered by non-target class examples. Though cost-SVM (cSVM)has been proposed to tackle the imbalanced datasets by assigning different cost functions to different classes, the performanceis less than satisfactory due to its limited ability to enforce cost-sensitivity. In this research, a generative classifier, GaussianMixture Model (GMM) is studied which can learn the distribution of the imbalanced data to improve the discriminative powerbetween imbalanced classes. By fusing this knowledge into cSVM, a model fusion approach, termed CSG (cSVM+GMM), isproposed to tackle the imbalanced classification problem. Experimental results on eleven benchmark datasets and one medicalimaging dataset show the effectiveness of CSG in dealing with imbalanced classification problems.