An empirical study on CO2 emissions in ASEAN countries

This paper proposes a local feature selection (FS) measure namely, Categorical Descriptor Term (CTD) for text categorization. It is derived based on classic term weighting scheme, TFIDF. The method explicitly chooses feature set for each category by only selecting set of terms from relevant category...

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Main Authors: Bong, Chih How, Narayanan, Kulathuramaiyer
Format: Proceeding
Language:English
Published: 2012
Subjects:
Online Access:http://ir.unimas.my/id/eprint/8478/
http://ir.unimas.my/id/eprint/8478/1/An%20Empirical%20Study%20of%20Feature%20Selection%20for%20Text%20Categorization%20based%20on%20Term%20%28abstract%29.pdf
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author Bong, Chih How
Narayanan, Kulathuramaiyer
author_facet Bong, Chih How
Narayanan, Kulathuramaiyer
author_sort Bong, Chih How
building UNIMAS Institutional Repository
collection Online Access
description This paper proposes a local feature selection (FS) measure namely, Categorical Descriptor Term (CTD) for text categorization. It is derived based on classic term weighting scheme, TFIDF. The method explicitly chooses feature set for each category by only selecting set of terms from relevant category. Although past literatures have suggested that the use of features from irrelevant categories can improve the measure of text categorization, we believe that by incorporating only relevant feature can be highly effective. The experimental comparison is carried out between CTD and five wellknown feature selection measures: Information Gain, Chi-Square, Correlation Coefficient, Odd Ratio and GSS Coefficient. The results also show that our proposed method can perform comparatively well with other FS measures, especially on collection with highly overlapped topics.
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institution Universiti Malaysia Sarawak
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language English
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publishDate 2012
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spelling unimas-84782015-08-05T01:54:42Z http://ir.unimas.my/id/eprint/8478/ An empirical study on CO2 emissions in ASEAN countries Bong, Chih How Narayanan, Kulathuramaiyer T Technology (General) This paper proposes a local feature selection (FS) measure namely, Categorical Descriptor Term (CTD) for text categorization. It is derived based on classic term weighting scheme, TFIDF. The method explicitly chooses feature set for each category by only selecting set of terms from relevant category. Although past literatures have suggested that the use of features from irrelevant categories can improve the measure of text categorization, we believe that by incorporating only relevant feature can be highly effective. The experimental comparison is carried out between CTD and five wellknown feature selection measures: Information Gain, Chi-Square, Correlation Coefficient, Odd Ratio and GSS Coefficient. The results also show that our proposed method can perform comparatively well with other FS measures, especially on collection with highly overlapped topics. 2012 Proceeding NonPeerReviewed text en http://ir.unimas.my/id/eprint/8478/1/An%20Empirical%20Study%20of%20Feature%20Selection%20for%20Text%20Categorization%20based%20on%20Term%20%28abstract%29.pdf Bong, Chih How and Narayanan, Kulathuramaiyer (2012) An empirical study on CO2 emissions in ASEAN countries. In: International Conference on Statistics in Science, Business, and Engineering (ICSSBE), 2012, 10-12 Sept. 2012, Langkawi. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6396635&tag=1
spellingShingle T Technology (General)
Bong, Chih How
Narayanan, Kulathuramaiyer
An empirical study on CO2 emissions in ASEAN countries
title An empirical study on CO2 emissions in ASEAN countries
title_full An empirical study on CO2 emissions in ASEAN countries
title_fullStr An empirical study on CO2 emissions in ASEAN countries
title_full_unstemmed An empirical study on CO2 emissions in ASEAN countries
title_short An empirical study on CO2 emissions in ASEAN countries
title_sort empirical study on co2 emissions in asean countries
topic T Technology (General)
url http://ir.unimas.my/id/eprint/8478/
http://ir.unimas.my/id/eprint/8478/
http://ir.unimas.my/id/eprint/8478/1/An%20Empirical%20Study%20of%20Feature%20Selection%20for%20Text%20Categorization%20based%20on%20Term%20%28abstract%29.pdf