Imbalanced Classification Based on Active Learning SMOTE

Abstract

In real-world problems, the data sets are typically imbalanced. Imbalance has a serious impact on the performance of classifiers. SMOTE is a typical over-sampling technique which can effectively balance the imbalanced data. However, it brings noise and other problems affecting the classification accuracy. To solve this problem, this study introduces the classification performance of support vector machine and presents an approach based on active learning SMOTE to classify the imbalanced data. Experimental results show that the proposed method has higher Area under the ROC Curve, F-measure and G-mean values than many existing class imbalance learning methods.