Self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification

Abstract Imbalanced data classification poses a major challenge in data mining community. Although standard support vector machine can generally show relatively robust performance in dealing with the classification problems of imbalanced data set, it is a typical overall accuracy-oriented algorithm which results in the final decision boundary biasing toward the majority class. Some ensemble methods have emerged as meta-techniques for improving the generalization performance of existing learning algorithms. In this paper, we propose a novel self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification. In the proposed approach, to guarantee the consistency of optimization objectives between weak learners and boosting scheme, we not only apply cost-sensitive SVMs as basic weak leaner but also simultaneously modify the standard boosting scheme to cost-sensitive ones. In order to ensure more training minority instances for successive classifiers, especially borderline minority instances, we also present a self-adaptive sequential misclassification cost weights determination method. The method can self-adaptively consider the different contribution of minority instances to the form of SVM classifiers at each iteration based on the preceding obtained classifier during boosting, which can allow it to produce diverse classifiers and thus improve its generalization performance. In the experiments, we analyze and discuss the effect of different parameters on the performance and some suggestions are also provided. The extensive experimental results on the different imbalanced datasets demonstrate that the proposed approach can achieve better generalization performance in terms of G-Mean and F-Measure as compared to the other existing imbalanced dataset classification techniques.