Latent Supportive Utility of Irrelevant Attributes in Feature SelectionOA
Latent Supportive Utility of Irrelevant Attributes in Feature Selection
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 …查看全部>>
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 da…查看全部>>
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Faculty of Science and Technology, University of Macau, Macau, China;Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;Faculty of Science and Technology, University of Macau, Macau, China;Faculty of Science and Technology, University of Macau, Macau, China
交通运输
Latent relevance Irrelevant feature selection Preprocessing Data mining
Latent relevance Irrelevant feature selection Preprocessing Data mining
《西南交通大学学报（英文版）》 2008 (1)
10-17,8
The Science and Technology Development Fund from Macau Government (No.007/2006/A)
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