NDLI: Feature Selection Using Recursive Feature Elimination for Handwritten Digit Recognition

Content Provider	IEEE Xplore Digital Library
Author	Xiangyan Zeng Yen-Wei Chen Caixia Tao van Alphen, D.
Copyright Year	2009
Abstract	In this paper, a new feature selection method with applications to handwritten digit recognition is proposed. This method is based on recursive feature elimination (RFE) in least squares support vector machines (LS-SVM). Digit recognition is achieved by one-against-all LS-SVMs. The RFE method is adapted to multi-class classification in two ways. One is to prune features for each binary LS-SVM classifier independently, and the other is to prune features for all the binary classifiers jointly. The multi-class RFE is also compared with the wrapper feature selection method which uses genetic algorithms. The experimental results indicate that the joint pruning algorithm yields the best performance and selects more features relevant to intrinsic characteristics of digits.
Starting Page	1205
Ending Page	1208
File Size	364284
Page Count	4
File Format	PDF
ISBN	9781424447176
DOI	10.1109/IIH-MSP.2009.145
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature selection recursive feature elimination multi-class classification Mathematics least squares support vector machine Least squares methods Genetic algorithms Support vector machines Handwriting recognition Filters Neural networks Support vector machine classification Signal processing handwritten digit recognition Feature extraction
Content Type	Text
Resource Type	Article

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