关键词: 不平衡分类, Lé, vy分布, 过采样, 人工合成过采样技术（SMOTE）

Abstract: For the classification problems on imbalanced datasets, a Lévy-based oversampling technique is proposed. Its essential idea is to employ Lévy distribution to construct the density distribution of synthetic samples according to the distribution of original datasets. Due to the properties of the Lévy distribution, the density of new samples synthetized from the borderlines is the largest, the density of new samples synthetized from the samples closer to the majority is the second one, and the density of new samples synthetized from the samples closer to the minority is the smallest. Thus, this approach can enhance the decision boundary and reduce the noise generation in the same time. Experiments on multiple datasets show that the proposed approach can effectively improve the classification results on imbalanced datasets.

Key words: imbalanced classification, Lévy distribution, oversampling, Synthetic Minority Oversampling Technique（SMOTE）