In last few years there are major changes and evolution has been done on
classification of data. As the application area of technology is increases the
size of data also increases. Classification of data becomes difficult because
of unbounded size and imbalance nature of data. Class imbalance problem become
greatest issue in data mining. Imbalance problem occur where one of the two
classes having more sample than other classes. The most of algorithm are more
focusing on classification of major sample while ignoring or misclassifying
minority sample. The minority samples are those that rarely occur but very
important. There are different methods available for classification of
imbalance data set which is divided into three main categories, the algorithmic
approach, data-preprocessing approach and feature selection approach. Each of
this technique has their own advantages and disadvantages. In this paper
systematic study of each approach is define which gives the right direction for
research in class imbalance problem.

本文系统地研究了数据分类中的类别不平衡问题，探讨了算法、数据预处理以及特征选择等方法的优劣，为类别不平衡问题的后续研究提供了正确方向。