DocumentCode
3337033
Title
Feature selection method based on the improved of mutual information and genetic algorithm
Author
Qiu Ye ; Liu Peiyu ; Yang Yuzhen
Author_Institution
Sch. of Inf. Sci. & Eng., Shandong Normal Univ., Ji´nan, China
Volume
1
fYear
2009
fDate
14-16 Aug. 2009
Firstpage
836
Lastpage
839
Abstract
The feature selection is a key method of text categorization technology, this paper proposed a text feature selection method based on the improved of mutual information and genetic algorithm. Used the improved of mutual information algorithm to do the initial choose to removing redundancy and noise words at first, and then used the genetic algorithm to training the template which generate by a subset of words, so get the optimal feature subset that on behalf of the issue space, to achieve dimensionality reduction and improved classification accuracy.
Keywords
feature extraction; genetic algorithms; set theory; text analysis; feature selection method; genetic algorithm; mutual information algorithm; optimal feature subset; text categorization technology; Computational complexity; Evolution (biology); Frequency; Genetic algorithms; Genetic engineering; Information science; Mutual information; Noise generators; Noise reduction; 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.2009.5236305
Filename
5236305
Link To Document