西南交通大学学报（英文版）2008，Vol.16Issue(1)：10-17,8.
Latent Supportive Utility of Irrelevant Attributes in Feature Selection
Latent Supportive Utility of Irrelevant Attributes in Feature Selection
摘要
Abstract
This paper proposed a novel feature selection method LUIFS (latent utility of irrelevant feature selection) that not only selects the relevant features, but also targets at discovering the latent useful irrelevant attributes by measuring their supportive importance to other attributes. The method minimizes the information lost and simultaneously maximizes the final classification accuracy. The classification error rates of the LUIFS method on 16 real-life datasets from UCI machine learning repository were evaluated using the ID3, Nave-Bayes, and IB (instance-based classifier) learning algorithms, respectively; and compared with those of the same algorithms with no feature selection (NoFS), feature subset selection (FSS), and correlation-based feature selection (CFS). The empirical results demonstrate that the LUIFS can improve the performance of learning algorithms by taking the latent relevance for irrelevant attributes into consideration, and hence including those potentially important attributes into the optimal feature subset for classification.关键词
Latent relevance/ Irrelevant feature selection/ Preprocessing/ Data miningKey words
Latent relevance/ Irrelevant feature selection/ Preprocessing/ Data mining分类
交通工程引用本文复制引用
..Latent Supportive Utility of Irrelevant Attributes in Feature Selection[J].西南交通大学学报（英文版）,2008,16(1):10-17,8.基金项目
The Science and Technology Development Fund from Macau Government (No.007/2006/A) （No.007/2006/A）
