An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

When building a classification model, the scenario where the samples of one class are significantly more than those of the other class is called data imbalance. Data imbalance causes the trained classification model to be in favor of the majority class (usually defined as the negative class), which may do harm to the accuracy of the minority class (usually defined as the positive class), and then lead to poor overall performance of the model. A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article, which is based on a new hybrid resampling approach (MSHR) and a new fine cost-sensitive support vector machine (CS-SVM) classifier (FCSSVM). The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples, based on which, the so-called pseudo-negative samples are screened out to generate new positive samples (over-sampling step) through linear interpolation and are deleted finally (under-sampling step). This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline, without changing the overall scale of the dataset. The FCSSVM is an improved version of the traditional CS-SVM. It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously, and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice (RIME) algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline. To verify the effectiveness of the proposed method, a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets. The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases, and both the MSHR and the FCSSVM played significant roles.