Feature selection for linear support vector machines

Authors:
Zhizheng Liang;Tuo Zhao
Affiliations:
Shenzhen Graduate School,HIT;Shenzhen Graduate School,HIT
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Year:
2006

Citing 0
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Abstract

Feature selection is attracted much interest from researchers in many fields such as pattern recognition and data mining. In this paper, a novel algorithm for feature selection is developed. The proposed algorithm uses the standard linear SVM algorithm and is performed in an iterative way. Feature selection is carried out by assigning weights to features. Experimental results on UCI data set and face images confirm the feasibility and validation of the proposed method.