计算机工程与应用2019，Vol.55Issue(21)：0-16,17.DOI:10.3778/j.issn.1002-8331.1907-0040
类别不均衡学习中的抽样策略研究
Research on Sampling Strategies in Class-Imbalanced Learning
摘要
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, includ-ing 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.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Key words
class-imbalanced learning/ensemble learning/undersampling/feature selection/support vector machine/syn-thetic minority oversampling technique/hybrid sampling分类
信息技术与安全科学引用本文复制引用
刘树栋,张可..类别不均衡学习中的抽样策略研究[J].计算机工程与应用,2019,55(21):0-16,17.基金项目
国家自然科学基金(No.61602518,No.71872180) （No.61602518,No.71872180）
中南财经政法大学中央高校基本科研业务费专项资金(No.2722019JCG074, No.2722019JCT035). （No.2722019JCG074, No.2722019JCT035）
