NRMCS: NOISE REMOVING BASED ON THE MCS

摘要：

MCS (Minimal Consistent Set) is one of the classical algorithms for minimal consistent subset selection problem. However, when noisy samples are present classification accuracy can suffer. In addition, noise affect the size of minimal consistent set. Therefore, removing noise is an important issue before sample selection. In this paper, an improvement approach based on MCS to select the representative samples is proposed. Compared with other algorithms which remove the noise by Wilson Editing in advance for the representative samples selection, this algorithm performs the processes of noise removing and samples selection simultaneously. According to this method, most noise can be deleted and the most representative samples can be identified and retained. The experiments show that the proposed method can greatly remove the redundant samples and noise as well as increase the accuracy of solutions when it is used for classification tasks.

关键词： MCS Sample Selection ICF Representative Subset Noise Wilson Editing

作者: XI-ZHAO WANG BO WU YU-LIN HE XIANG-HAO PEI

作者单位: Key Lab.of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Baoding, 071002, Hebei, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 89-93

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）