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Categorical term descriptor: a proposed term weighting scheme for feature selection

This paper proposes a term weighting scheme, categorical term descriptor (CTD), for feature selection in automated text categorization. CTD is an adaptation of the term frequency inverse document frequency (TFIDF). We compared the performance of the proposed method against classical methods such as...

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Main Authors: Bong, Chih How, Kulathuramaiyer, Narayanan, Wong, Ting Kiong
格式: Conference or Workshop Item
语言:English
出版: IEEE 2005
主题:
AC Collections. Series. Collected works
T Technology (General)
在线阅读:http://ir.unimas.my/id/eprint/1196/1/categorical%2Bterm%2Bdescriptor%2B%2BA%2Bproposed%2Bterm%2Bweighting%2Bscheme%2Bfor%2Bfeature%2Bselection%2528abstract%2529.pdf
http://ir.unimas.my/id/eprint/1196/
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id my.unimas.ir.1196
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spelling my.unimas.ir.11962015-03-24T00:48:35Z http://ir.unimas.my/id/eprint/1196/ Categorical term descriptor: a proposed term weighting scheme for feature selection Bong, Chih How Kulathuramaiyer, Narayanan Wong, Ting Kiong AC Collections. Series. Collected works T Technology (General) This paper proposes a term weighting scheme, categorical term descriptor (CTD), for feature selection in automated text categorization. CTD is an adaptation of the term frequency inverse document frequency (TFIDF). We compared the performance of the proposed method against classical methods such as correlation coefficient, chi-square and information gain using the multinomial naive Bayes and the support vector machine (SVKD) classifiers on the Reuters (10) and Reuters (115) variants of Reuters-21578 dataset. IEEE 2005 Conference or Workshop Item NonPeerReviewed text en http://ir.unimas.my/id/eprint/1196/1/categorical%2Bterm%2Bdescriptor%2B%2BA%2Bproposed%2Bterm%2Bweighting%2Bscheme%2Bfor%2Bfeature%2Bselection%2528abstract%2529.pdf Bong, Chih How and Kulathuramaiyer, Narayanan and Wong, Ting Kiong (2005) Categorical term descriptor: a proposed term weighting scheme for feature selection. In: 2005 IEEENVlClACM International Conference on Web Intelligence (W1'05).
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic AC Collections. Series. Collected works
T Technology (General)
spellingShingle AC Collections. Series. Collected works
T Technology (General)
Bong, Chih How
Kulathuramaiyer, Narayanan
Wong, Ting Kiong
Categorical term descriptor: a proposed term weighting scheme for feature selection
description This paper proposes a term weighting scheme, categorical term descriptor (CTD), for feature selection in automated text categorization. CTD is an adaptation of the term frequency inverse document frequency (TFIDF). We compared the performance of the proposed method against classical methods such as correlation coefficient, chi-square and information gain using the multinomial naive Bayes and the support vector machine (SVKD) classifiers on the Reuters (10) and Reuters (115) variants of Reuters-21578 dataset.
format Conference or Workshop Item
author Bong, Chih How
Kulathuramaiyer, Narayanan
Wong, Ting Kiong
author_facet Bong, Chih How
Kulathuramaiyer, Narayanan
Wong, Ting Kiong
author_sort Bong, Chih How
title Categorical term descriptor: a proposed term weighting scheme for feature selection
title_short Categorical term descriptor: a proposed term weighting scheme for feature selection
title_full Categorical term descriptor: a proposed term weighting scheme for feature selection
title_fullStr Categorical term descriptor: a proposed term weighting scheme for feature selection
title_full_unstemmed Categorical term descriptor: a proposed term weighting scheme for feature selection
title_sort categorical term descriptor: a proposed term weighting scheme for feature selection
publisher IEEE
publishDate 2005
url http://ir.unimas.my/id/eprint/1196/1/categorical%2Bterm%2Bdescriptor%2B%2BA%2Bproposed%2Bterm%2Bweighting%2Bscheme%2Bfor%2Bfeature%2Bselection%2528abstract%2529.pdf
http://ir.unimas.my/id/eprint/1196/
_version_ 1644508804601085952
score 13.251813

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