TS-WRSVM: twin structural weighted relaxed support vector machine

ABSTRACTClassification of data with imbalanced class distributions is a major problem in the data mining community. Imbalanced classification is a challenging task in the presence of outliers. In this paper, we propose a new cost-sensitive learning method with regard to the structure of data distribution for classifying imbalanced data and diminishing the effect of outliers. The proposed method combines the benefits of “structured” learning models (such as structural support vector machine) with the advantages of “cost-sensitive” learning models (such as weighted relaxed support vector machine). We call our method twin structural weighted relaxed support vector machine (TS-WRSVM). A TS-WRSVM uses two nonparallel hyperplanes to determine the class label of new data so that each model only considers the structural information of one class. We allocate a weight and a limited amount of penalty-free slack to each model by considering the size of each class. Results of experiments indicate that a TS-WRSVM is su...