关键词: 不平衡数据集, 分类, 遗传算子, 少数类样本合成过采样技术（SMOTE）

Abstract: Based on analyzing the shortages of SMOTE (synthetic minority over-sampling technique) in the synthesis of minority class samples, this paper presents an improved SMOTE (GA-SMOTE). The key of GA-SMOTE lies on leading three basic genetic operators of genetic algorithm (GA) into SMOTE, making use of the selection operator to achieve the different samples from the minority class and depending on crossover operator and mutation operator to realize the fine control of the synthesis quality to the minority class samples. GA-SMOTE and SVM (support vector machine) are combined to handle the classification problem on imbalanced datasets. A large amount of experiments on the UCI datasets show that GA-SMOTE promises prominent synthesis effect to the minority class samples, and brings better classification performance on imbalanced datasets with SVM.

Key words: imbalanced dataset, classification, genetic operator, synthetic minority over-sampling technique (SMOTE)