Trained SVMs based rules extraction method for text classification

Author

Zhang, Miao ; Zhang, De-Xian

Author_Institution

Coll. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou

fYear

2008

fDate

12-14 Dec. 2008

Firstpage

16

Lastpage

19

Abstract

The automatic text classification method aims to assign text files to one or more predefined categories according to the text information contained by all kinds of text format files. SVM is recognized as one of the most effective text classification methods for its high accuracy, but its black-box feature causes that the description of each category can not be given and explained. In this paper, a new rule extraction method for text classification based on trained SVMs is proposed to solve the bottleneck of SVMs. The experiments show that the proposed approach can improve the validity of the extracted rules remarkably compared to C4.5 either in speed or accuracy.

Keywords

classification; knowledge based systems; support vector machines; text analysis; automatic text classification; rules extraction; support vector machine; text format file; Data mining; Educational institutions; Educational technology; Information resources; Information science; Support vector machine classification; Support vector machines; Text categorization; Text recognition; Velocity measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

IT in Medicine and Education, 2008. ITME 2008. IEEE International Symposium on

Conference_Location

Xiamen

Print_ISBN

978-1-4244-3616-3

Electronic_ISBN

978-1-4244-2511-2

Type

conf

DOI

10.1109/ITME.2008.4743814

Filename

4743814