Presentation 2006-01-12
Realization of Text Classification Systems by Approximation of Classification Functions based on the Genetic Programming and its Applications
Noboru TAKAGI, Shozo TOKINAGA,
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Abstract(in English) This paper deals with the realization of text classification systems by using sequence estimation of words and approximation of classification functions based on the Genetic Programming (GP) and its Applications. Conventional text classification methods utilize the feature vector corresponding to frequecy of appearance of key-words, but the method can not provide us sufficient results. In the paper, we apply the GP procedure to approximate nonlinear classification functions in which the elements of feature vector are used for the input variables. The method of the paper is applied to simulation studies for artificially generated text, and also to theclassification of real article in the field of economics.
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Keyword(in English) Rule estimation / Genetic Programming / categorization
Paper # CAS2005-77
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Committee CAS
Conference Date 2006/1/5(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Realization of Text Classification Systems by Approximation of Classification Functions based on the Genetic Programming and its Applications
Sub Title (in English)
Keyword(1) Rule estimation
Keyword(2) Genetic Programming
Keyword(3) categorization
1st Author's Name Noboru TAKAGI
1st Author's Affiliation Dept. of Management and Infomation Science, Nagasaki Institute of Applied Science First University()
2nd Author's Name Shozo TOKINAGA
2nd Author's Affiliation Graduate School of Economics, Kyushu Univ.
Date 2006-01-12
Paper # CAS2005-77
Volume (vol) vol.105
Number (no) 503
Page pp.pp.-
#Pages 5
Date of Issue