Applying instance-weighted support vector machines to class imbalanced datasets

Abstract

Learning with class imbalance is always a challenging task in many real world applications such as the Internet, surveillance, security, and finance. Like many other successful machine learning algorithms, the success of the support vector machine (SVM) is limited when it is applied to the problem of learning from imbalanced datasets. SVM with different error costs has been widely used to deal with the class imbalanced problem. In this paper, we are trying to apply an instance-weighted variant of the SVM with both 1-norm and 2-norm format to deal with the class imbalance problem. We develop an asymmetric boosting method on the weights of the tradeoff parameters to optimize the instance-weighted SVM. The experimental results on the benchmark datasets show that the proposed algorithm is effective on the class imbalanced problem.