关键词: 不平衡数据, 过采样技术, 合成少数类过采样技术（SMOTE）, 构造性覆盖算法（CCA）

Abstract:

Improving the recognition ability of minority samples is a crucial research hotspot of imbalance data classification. Synthetic minority over-sampling technique (SMOTE) is a typical representative technique to solve such problem. In recent years, researchers have made some improvements on SMOTE, and the performance of this method is improved. However, how to select the most informative minority samples efficiently for over-sampling still needs to be improved. Moreover, the potential ability of isolate minority samples in improving model performance does??t get enough attention. In this paper, an over-sampling technique based on constructive covering algorithm (CCA) and SMOTE (namely CMOTE) is proposed to solve the above problems. CMOTE provides two CCA based strategies (one is based on the number of samples in cover and one is based on cover density) in selecting key samples. Numerical experiments on 12 typical imbalance datasets are conducted to verify the performance of CMOTE. Experimental results show that CMOTE is generally superior to the algorithms compared. The generalization ability of the model is enhanced by strengthening the impact of critical samples on model performance.

Key words: imbalanced data, over-sampling technique, synthetic minority over-sampling technique (SMOTE), cons-tructive covering algorithm (CCA)