%0 Journal Article
%T Overview of classification algorithms for unbalanced data<br>不均衡数据分类算法的综述
%A TAO Xinmin
%A HAO Siyuan
%A ZHANG Dongxue
%A XU Peng
%A <br>陶新民
%A 郝思媛
%A 张冬雪
%A 徐鹏
%J 重庆邮电大学学报(自然科学版)
%D 2013
%I 
%X 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.
%K unbalanced data
%K improved approaches
%K classification performance<br>不均衡数据
%K 改进算法
%K 分类性能
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=01BA20E8BA813E1908F3698710BBFEFEE816345F465FEBA5&cid=96E6E851B5104576C2DD9FC1FBCB69EF&jid=5C2694A2E5629ECD6B59D7B28C6937AD&aid=1D447377FA3F59D044B071BC9047BB91&yid=FF7AA908D58E97FA&iid=CA4FD0336C81A37A&sid=74011071555EB4E5&eid=4DB1E72614E68564&journal_id=1673-825X&journal_name=重庆邮电大学学报(自然科学版)&referenced_num=0&reference_num=0