NDLI: An adaptive feature selection method for multi-class classification

Content Provider	IEEE Xplore Digital Library
Author	Xin Xu Wei Wang Guilin Zhang Yongsheng Yu
Copyright Year	2012
Description	Author affiliation: Science and Technology on Information System Engineering Laboratory, Nanjing, China 210007 (Xin Xu; Guilin Zhang; Yongsheng Yu) \|\| Nanjing Institute of ZTE Corporation, Nanjing, China 210012 (Wei Wang)
Abstract	A vast variety of feature selection methods have been proposed according to different metrics, such as information gain, entropy, chi-square test, t-test. Yet when applied to multi-class classification task, these methods generally suffer the “siren pitfall” of a surplus of predictive features for some classes while lack of predictive features for the remaining classes. A number of solutions to the “siren pitfall” have been proposed, yet there are still problems with these methods. For example, the selected features by “randomized feature set” method are not re-configurable; the “rand-robin” method and the “round-robin” method may miss some important features whenever the one v.s. others binary partition does not work; the computation cost of the wrapper's method is rather high. In this paper, we propose an adaptive feature selection method for multi-class classification task. With our method, the “siren pitfall” could be avoided, the selected features could be reproduced, the feature selection scheme does not rely on any prior knowledge, and the corresponding computation cost is low. Experimental results indicate the effectiveness of our adaptive feature selection method.
Starting Page	225
Ending Page	230
File Size	526043
Page Count	6
File Format	PDF
ISBN	9781467304177
e-ISBN	9780982443859
e-ISBN	9780982443842
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-09
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	ISIF ( Intl Society of Information Fusi
Subject Keyword	Encoding Computational efficiency Error analysis Measurement Mixed integer linear programming Algorithms Lenses classification feature selection
Content Type	Text
Resource Type	Article

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