Synthetic Minority Oversampling Technique Based on Adaptive Local Mean Vectors and Improved Differential Evolution

Abstract

SMOTE is a classical oversampling method and aims to improve imbalanced classification by creating synthetic minority class samples. Overgeneralization is a great challenge in SMOTE and its improvements. Multiple variations of SMOTE are proposed against imbalances between classes and overgeneralization. However, they still have the following issues: 1) most methods depend on too many parameters; 2) most methods fail to detect suspicious noise effectively and modify them; 3) interpolation of almost all methods is susceptible to abnormal samples. To overcome the above issues, a new synthetic minority oversampling technique based on adaptive local mean vectors and improved differential evolution (SMOTE-LMVDE) is proposed. First, a new noise detection technique based on the defined adaptive local mean vectors (NDALMV) is proposed to find suspicious noise. Second, an improved differential evolution is proposed to modify and improve detected suspicious noise. Finally, a new interpolation based on the defined adaptive local mean vectors is proposed to create synthetic minority class samples. Experiments prove that the proposed method superior to 7 popular oversampling approaches on extensive data sets in the training nearest neighbor classifier and the decision tree classifier.

Keywords

• Imbalanced learning
• imbalanced classification
• classification
• oversampling
• local means
• differential evolution