Improved over-sampling techniques based on sparse representation for imbalance problem

Abstract

The classification problem of imbalanced datasets has received much attention in recent years. This imbalance problem usually occurs in when the ratio between classes is high. Many techniques have been developed to tackle the imbalance problem in supervised learning. The Synthetic Minority Over-sampling Technique (SMOTE) is one of the most effective over-sampling methods processing this problem, which changes the distribution of training sets to balance the different number of examples of each class. However, SMOTE randomly synthesizes the minority instances along a line joining a minority instance and its selected nearest neighbors, ignoring nearby majority instances and isolated points, which would affect the final classification result. In this paper, we propose two improved techniques based on SMOTE through sparse representation theory. This extension results in Sparse-SMOTE and SROT (Sparse Representation Based Over-Sampling Technique). The Sparse-SMOTE replaces the k-nearest neighbors of the SMOTE with sparse representation, and the SROT uses a sparse dictionary to create a synthetic sample directly. The experiments are performed on 10 UCI datasets using C4.5 as the learning algorithm. The experimental results show that both proposed methods can achieve better performance on TP-Rate, F-Measure, G-Mean and AUC values. Moreover, the results show that our new proposals’ perform is more effective compared with SMOTE and some other approaches.