Research on classifying technique for imbalanced dataset based on support vector machines

It is shown that SVM can be ineffective in classifying the minority samples, when it is applied to the problem of learning from imbalanced datasets. To remedy this problem, this paper analyzes the true reason of negative effect to SVM classifier caused by data imbalance firstly. Based on this, a new method of shifting classifying hyperplane in the feature space is proposed, and its implementation method-Boundary Movement based on Sample Cutting Technique (BMSCT) is also described. Through theoretical analysis and empirical study, we show that our method augments the classification accuracy rate effectively without increasing the computation complexity.