不均衡数据分类算法的综述

传统的分类方法都是建立在类分布大致平衡这一假设基础上的,然而实际情况中,数据往往都是不均衡的.因此,传统分类器分类性能通常比较有限.从数据层面和算法层面对国内外分类算法做了详细而系统的概述.并通过仿真实验,比较了多种不平衡分类算法在6个不同数据集上的分类性能,发现改进的分类算法在整体性能上得到不同程度的提高,最后列出了不均衡数据分类发展还需解决的一些问题.

Overview of classification algorithms for unbalanced data

Traditional classification methods are based on the assumption that the training sets are well-balanced, however, in real case the data is usually unbalanced, and the classification performance of the traditional classification is always restricted. A detailed overview of domestic and foreign classification algorithms from the data level and algorithm level is provided in this paper. And through simulation experiments to compare the classification performance of a variety of unbalanced classification algorithm on six different data sets, it is found that the improved classification algorithm has varying degrees of improvement for overall performance. The paper concludes with a list of problems which need solving for the development of unbalanced data classification.