关键词: 不均衡学习, 集成学习, 欠抽样, 特征选择, 支持向量机, 合成少数类过抽样技术, 混合抽样

Abstract: Class-imbalanced learning has been widely used in many application domains, such as credit scoring, customer churn prediction, medical diagnosis, short-text sentiment analysis, label learning, review prediction, which has become one of the hottest topics in domain of machine learning and its applications, and are attracting more and more attention from both industry and academia recently. A great variety of solutions have been proposed to address class imbalance problem, which can be generally divided into three groups: data-level solutions, algorithm-level solutions and ensemble solutions. This paper presents an overview of the field of sampling strategies in class-imbalanced learning, which are more important methods in data-level solutions. This paper introduces the basic issue of class-imbalanced learning, including the formal definition, performance metrics and the basic framework, reviews in detail the recent development of over-sampling, under-sampling and hybrid sampling, which are three main sampling strategies in class-imbalanced learning. The prospects for future development and suggestions for possible extensions are also discussed.

Key words: class-imbalanced learning, ensemble learning, undersampling, feature selection, support vector machine, synthetic minority oversampling technique, hybrid sampling