不平衡数据分类研究综述

在很多应用领域中,数据的类别分布不平衡,如何对其正确分类是数据挖掘和机器学习领域中的研究热点。经典的数据分类算法未考虑数据类别的不平衡性,认为类别之间的误分类代价相同,导致不平衡数据分类的效果不理想。针对数据分类的各个步骤,相继提出了不同的不平衡数据分类处理方法。对多年来的相关研究成果进行归类分析,从特征选择、数据分布调整、分类算法、分类结果评估等几个方面系统地介绍了相关方法,并探讨了进一步的探索方向。

Overview of Imbalanced Data Classification

Imbalanced data classification has been drawn significant attention from research community in last decade.Because of the assumption of relatively balanced class distribution and equal misclassification costs,most standard classifiers do not perform well with imbalanced data classification.In view of various phases of data classification,different imbalanced data classification methods have been proposed.The relevant research achievements over the years were analyzed,and various approaches with imbalanced data were introduced from the view of feature selection,adjustment of the data distribution,classification algorithm and classifier evaluation.The future trends and research issues that still need to be faced in imbalanced data classification were discussed in the end.