Optimization of text feature subsets based on GATS algorithm

Author

Jiang, Pei-Pei ; Liu, Pei-Yu ; Zhu, Zhen-Fang ; Zhao, Li-Na

Author_Institution

Dept. of Inf. Sci. & Eng., Shandong Normal Univ., Jinan, China

Volume

1

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

924

Lastpage

927

Abstract

For feature subset optimization problems in text categorization, the GATS strategy which combines the genetic algorithm with taboo search algorithm is proposed in this paper to be applied to text categorization to realize the dimensionality reduction of the feature space. The experiments show that the application of this method to select the characteristics of the text can not only maintain the advantages of the GA and the TS algorithm themselves, but also improve the classification accuracy of the text.

Keywords

combinatorial mathematics; genetic algorithms; mathematical operators; pattern classification; search problems; text analysis; GATS algorithm; dimensionality reduction; feature subset optimization problems; genetic algorithm with taboo search algorithm; text categorization; text feature subsets; Approximation algorithms; Data mining; Genetic algorithms; Guidelines; Information retrieval; Internet; Machine learning algorithms; Optimization methods; Space technology; Text categorization;

fLanguage

English

Publisher

ieee

Conference_Titel

IT in Medicine & Education, 2009. ITIME '09. IEEE International Symposium on

Conference_Location

Jinan

Print_ISBN

978-1-4244-3928-7

Electronic_ISBN

978-1-4244-3930-0

Type

conf

DOI

10.1109/ITIME.2009.5236207

Filename

5236207