关键词: 非平衡数据, 近邻计算策略, 集成学习, 过采样

Abstract:

Oversampling method is one of the effective ways to deal with imbalance classification problems. This paper focuses on the problems of reducing sampling result faced by the oversampling methods based on SMOTE(synthetic minority oversampling technique) in the occurrence of class-overlapping and small-disjuncts in dataset. An oversampling method MOLAD based on the local area density is proposed. Furthermore, a method LADBMOTE which combines the MOLAD and Bagging-based ensemble learning in sampling stage is proposed in order to solve the classification problem for imbalanced dataset. LADBMOTE first calculates the [K] nearest neighbors of each minority class sample according to MOLAD, and then selects all [K] nearest neighbors for sampling, thus [K] balanced datasets will be generated. Then, the Bagging-based ensemble learning is used to ensemble classifiers obtained from training [K] balanced datasets. The proposed method LADBMOTE is compared with 7 currently popular algorithms for handling imbalanced data by employing 20 imbalanced datasets published on KEEL. The experimental results show that the classification performance of LADBMOTE on different classifiers is better and more robust.

Key words: imbalanced data, strategy for calculating nearest neighbors, ensemble learning, oversampling