LAD-SMOTE: A New Oversampling Method Based on Locally Adaptive Distance

Abstract

In imbalanced learning problems, classifiers bias the majority class which would lead to poor predictive accuracy over the minority class. Synthetic minority oversampling technique (SMOTE) is one of the oversampling methods for imbalanced learning problems. However SMOTE may cause over-generalizing. Existing methods for improving SMOTE usually only select the borderline minority samples to over-sample. In this way, some non-borderline informative minority samples can not be fully taken into consideration. To overcome this issue, a new oversampling method named LAD-SMOTE is proposed in this paper based on locally adaptive distance. Both borderline minority samples and non-borderline informative minority samples are over-sampled by our method. In LAD-SMOTE, non-borderline informative minority samples can be fully learned and thus over-generalizing caused by SMOTE can be alleviated. Experimental results show that the algorithm can efficiently improve the classification performance on imbalanced data sets than some existing ones.