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 :
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