关键词: 非平衡数据集，分类，抽样技术，代价敏感学习

Abstract: Because of imbalanced class distribution, most classifiers lose efficiency with it. In fact the rarely occurring class in imbalanced datasets shows statistical significance. The problem of learning from imbalanced datasets has attracted growing attention in recent years. The paper provide a comprehensive review of the classification of imbalanced datasets, the nature of the problem, the factor which affected the problem, the current assessment metrics used to evaluate learning performance, as well as the opportunities and challenges in the learning from imbalanced data.

Key words: Imbalanccd data sets,Classification,Sampling methods,Cost-scnsitive learning