关键词: 不平衡数据, 采样, 聚类方法, 集成方法, 代价敏感, 性能评估

Abstract: In recent years, the classification algorithms have made great progress. But as data sources continue to expand, most of the obtained data are unbalanced. These classification algorithms are usually sensitive to unbalanced data, so the classification of unbalanced data becomes very difficult. At present, the unbalanced data mining methods are mainly divided into two aspects, which are preprocessing methods and mining algorithms for unbalanced data. This paper summarizes the two aspects of the methods and makes a multi-dimensional combing from data preprocessing, algorithms and performance evaluation methods in recent years. Then, starting from different application fields, this paper describes all kinds of the unbalanced data problems, as well as the research and solutions of different scholars in their fields. Finally, the existing problems in the field of unbalanced data mining are analyzed, and the future research directions are prospected.

Key words: imbalanced data, sampling, cluster method, ensemble method, cost sensitive, performance evaluation