AN INSTANCE SELECTION ALGORITHM BASED ON CONTRIBUTION

摘要：

This paper presents an approach to instance selection for the nearest neighbor rule which aims to obtain a condensed set with high condensing rate and prediction accuracy. By making an improvement on MCS algorithm and allowing certain error rate on the training set, a condensed set with high condensing rate and satisfying prediction accuracy is obtained. The condensed set is order-independent of the training instances and insensitive to noise. Comparative experiments have been conducted on real data sets, and the results show its superiority to MCS and FCNN in terms of condensing rate and prediction accuracy.

关键词： Instance selection nearest neighbor rule condensed set MCS FCNN

作者: NING ZHANG XI-ZHAO WANG TAO XIAO

作者单位: Key Lab.for Machine Learning and Computational Intelligence, College of Mathematics and Computer Sci College of Science, Agricultural University of Hebei, Baoding 071002, 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: 919-923

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）