DocumentCode :
567453
Title :
An adaptive feature selection method for multi-class classification
Author :
Xu, Xin ; Wang, Wei ; Zhang, Guilin ; Yu, Yongsheng
Author_Institution :
Sci. & Technol. on Inf. Syst. Eng. Lab., Nanjing, China
fYear :
2012
fDate :
9-12 July 2012
Firstpage :
225
Lastpage :
230
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.
Keywords :
feature extraction; adaptive feature selection method; binary partition; chi-square test; entropy; information gain; multiclass classification; randomized feature set method; round-robin method; siren pitfall; t-test; Algorithms; Computational efficiency; Encoding; Error analysis; Lenses; Measurement; Mixed integer linear programming; classification; feature selection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2012 15th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4673-0417-7
Electronic_ISBN :
978-0-9824438-4-2
Type :
conf
Filename :
6289808
Link To Document :
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