Class imbalance is one of the challenging problems for machine learning in many real-world applications. Cost-sensitive learning has attracted significant attention in recent years to solve the problem, but it is difficult to determine the precise misclassification costs in practice. There are also other factors that influence the performance of the classification including the input feature subset and the intrinsic parameters of the classifier. This paper presents an effective wrapper framework incorporating the evaluation measure (AUC and G-mean) into the objective function of cost sensitive SVM directly to improve the performance of classification by simultaneously optimizing the best pair of feature subset, intrinsic parameters and misclassification cost parameters. Experimental results on various standard benchmark datasets and real-world data with different ratios of imbalance show that the proposed method is effective in comparison with commonly used sampling techniques.
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