Feature selection for text classification using OR+SVM-RFE

Author

Luo, Meixiang ; Luo, Linkai

Author_Institution

Coll. of Inf. Sci. & Technol., Xiamen Univ., Xiamen, China

fYear

2010

fDate

26-28 May 2010

Firstpage

1648

Lastpage

1652

Abstract

Feature selection is the key issue in text classification because there are a large number of attributes. In this paper, we propose a new algorithm OR+SVM-RFE that integrates Odds Radio(OR) with recursive feature elimination based on SVM(SVM-RFE). Odds Radio is first used to roughly and rapidly select a feature subset. Then SVM-RFE is used to delicately select a smaller feature subset. Experiment results show the feature subset selected by OR+SVM-RFE obtains a good classification performance with less features.

Keywords

classification; feature extraction; support vector machines; text analysis; OR+SVM-RFE algorithm; feature selection; feature subset; odds radio; recursive feature elimination; support vector machine; text classification; Classification algorithms; Classification tree analysis; Educational institutions; Information science; Internet; Machine learning; Machine learning algorithms; Support vector machine classification; Support vector machines; Text categorization; Feature Selection; Odds Ratio; SVM-RFE; Text Classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498331

Filename

5498331