TS-SMOTE: An Improved SMOTE Method Based on Symmetric Triangle Scoring Mechanism for Solving Class-Imbalanced Problems

The imbalanced classification problem is a key research in machine learning as the relevant algorithms tend to focus on the features and patterns of the majority class instead of insufficient learning of the minority class, resulting in unsatisfactory performance of machine learning. Scholars have attempted to solve this problem and proposed many ideas at the data and algorithm levels. The SMOTE (Synthetic Minority Over-sampling Technique) method is an effective approach at the data level. In this paper, we propose an oversampling method based on SMOTE and symmetric regular triangles scoring mechanism. This method uses symmetrical triangles to flatten the plane, and then establishes a suitable scoring mechanism to select the minority samples that participate in the synthesis. After selecting the minority samples, it conducts multiple linear interpolations according to the established rules to generate new minority samples. In the experimental section, we select 30 imbalanced datasets to test their performance of the proposed method and some classical oversampling methods under different indicators. In order to demonstrate the performance of these oversampling methods with classifiers, we select three different classifiers and test their performance. The experimental results show that the TS-SMOTE method has the best performance.