Bootstrap-based SVM aggregation for class imbalance problems

Support Vector Machines (SVMs) are considered to be one of the most powerful classification tools, widely used in many applications. However, in numerous scenarios the classes are not equally represented and the predictive performance of SVMs on such data can drop dramatically. Different methods have been proposed to address moderate class imbalance issues, but there are few methods that can be successful at detecting the minority class while also keeping high accuracy, especially when applied to datasets with significant level of imbalance. In this paper, we consider SVM ensembles that are built by using a bootstrap-based undersampling technique. We target to reduce the bias induced by class imbalances via multiple undersampling procedures and then decrease the variance using SVM ensembles. For combining the SVMs, we propose a new technique that deals with class imbalance problems of varying levels. Experiments on several datasets demonstrate the performance of the proposed scheme compared to state-of-the-art balancing methods.