DocumentCode :
2465993
Title :
Feature Selection Using Recursive Feature Elimination for Handwritten Digit Recognition
Author :
Zeng, Xiangyan ; Chen, Yen-wei ; Tao, Caixia ; Van Alphen, D.
Author_Institution :
Dept. of Math. & Comput. Sci., Fort Valley State Univ., Fort Valley, GA, USA
fYear :
2009
fDate :
12-14 Sept. 2009
Firstpage :
1205
Lastpage :
1208
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.
Keywords :
handwritten character recognition; least squares approximations; recursive estimation; support vector machines; feature selection; handwritten digit recognition; least squares support vector machines; recursive feature elimination; Feature extraction; Filters; Genetic algorithms; Handwriting recognition; Least squares methods; Mathematics; Neural networks; Signal processing; Support vector machine classification; Support vector machines; Feature selection; handwritten digit recognition; least squares support vector machine; multi-class classification; recursive feature elimination;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. Fifth International Conference on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4244-4717-6
Electronic_ISBN :
978-0-7695-3762-7
Type :
conf
DOI :
10.1109/IIH-MSP.2009.145
Filename :
5337549
Link To Document :
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