Pruning support vectors for imbalanced data classification

Abstract

In many practical applications, learning from imbalanced data poses a significant challenge that is increasingly faced by the machine learning community. The class imbalance problem raises issues that are either nonexistent or less severe compared to balanced class cases. This paper presents a new method for imbalanced data classification. The proposed method is based on support vector machine classifiers and backward pruning technique. The experimental results obtained on two data sets demonstrate the effectiveness of the new algorithm.