Abstract-Traditional classification algorithms can be limited in their performance on highly unbalanced data sets. A popular stream of work for countering the problem of class imbalance has been the application of a sundry of sampling strategies. In this correspondence, we focus on designing modifications to support vector machines (SVMs) to appropriately tackle the problem of class imbalance. We incorporate different "rebalance" heuristics in SVM modeling, including cost-sensitive learning, and over-and undersampling. These SVM-based strategies are compared with various state-of-the-art approaches on a variety of data sets by using various metrics, including G-mean, area under the receiver operating characteristic curve, F -measure, and area under the precision/recall curve. We show that we are able to surpass or match the previously known best algorithms on each data set. In particular, of the four SVM variations considered in this correspondence, the novel granular SVMs-repetitive undersampling algorithm (GSVM-RU) is the best in terms of both effectiveness and efficiency. GSVM-RU is effective, as it can minimize the negative effect of information loss while maximizing the positive effect of data cleaning in the undersampling process. GSVM-RU is efficient by extracting much less support vectors and, hence, greatly speeding up SVM prediction.
Another study by Tang and Chawla REF to compare four variations of SVMs, shows that the application of granular SVMs-reptitive undersampling algorithm (GSVM-RU) outperformed the other variations of SVMs in aspects like effectiveness and efficiency.
SVMs Modeling for Highly Imbalanced Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Computer Science Medicine