Text classification using Naive Bayes: An experiment to conference paper
The basic text classification technique in forum application has been discussed in Sainin (2005a) and Sainin (2005b).The paper explains about the use of the basic naïve Bayes algorithm to classify forum text me ssages into two classes namely clean and bad. In the problem of document text classific...
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Online Access: | http://repo.uum.edu.my/12432/1/ICOQSIA_shamrie.pdf http://repo.uum.edu.my/12432/ http://lintas.uum.edu.my:8080/elmu/index.jsp?module=webopac-l&action=fullDisplayRetriever.jsp&szMaterialNo=0000332840 |
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my.uum.repo.124322014-10-26T08:30:46Z http://repo.uum.edu.my/12432/ Text classification using Naive Bayes: An experiment to conference paper Sainin, Mohd Shamrie HD28 Management. Industrial Management The basic text classification technique in forum application has been discussed in Sainin (2005a) and Sainin (2005b).The paper explains about the use of the basic naïve Bayes algorithm to classify forum text me ssages into two classes namely clean and bad. In the problem of document text classification for conference paper, various papers themes will normally be classified manually by the conference management.Once the classification of the papers is ready, the parallel sessions for presentation according to the themes will be scheduled. This process is time consuming and may classify papers into unrelated themes.Based on this situation, an automated text document classification can replace the manual classification; hence reduce the decision time.In this paper, the similar algorithm that was applied in the previous experiment for the forum messages classification will be discussed according to the experiment for conference paper classification. 2005-12-06 Conference or Workshop Item NonPeerReviewed application/pdf en http://repo.uum.edu.my/12432/1/ICOQSIA_shamrie.pdf Sainin, Mohd Shamrie (2005) Text classification using Naive Bayes: An experiment to conference paper. In: International Conference on Quantitative Sciences and Its Applications 2005 (ICOQSIA 2005), 6-8, December 2005, Penang, Malaysia. (Unpublished) http://lintas.uum.edu.my:8080/elmu/index.jsp?module=webopac-l&action=fullDisplayRetriever.jsp&szMaterialNo=0000332840 |
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HD28 Management. Industrial Management Sainin, Mohd Shamrie Text classification using Naive Bayes: An experiment to conference paper |
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The basic text classification technique in forum application has been discussed in Sainin (2005a) and Sainin (2005b).The paper explains about the use of the basic naïve Bayes algorithm to classify forum text me
ssages into two classes namely clean and bad.
In the problem of document text classification for conference paper, various papers themes will normally be classified manually by the conference management.Once the classification of the papers is ready, the parallel sessions for presentation according to the themes will be scheduled. This process is time consuming and may classify papers into unrelated themes.Based on this situation, an automated text document classification can replace the manual classification; hence reduce the decision time.In this paper, the similar algorithm that was applied in the previous experiment for the forum messages classification will be discussed according to the experiment for conference paper classification. |
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Conference or Workshop Item |
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Sainin, Mohd Shamrie |
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Sainin, Mohd Shamrie |
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Sainin, Mohd Shamrie |
title |
Text classification using Naive Bayes: An experiment to conference paper |
title_short |
Text classification using Naive Bayes: An experiment to conference paper |
title_full |
Text classification using Naive Bayes: An experiment to conference paper |
title_fullStr |
Text classification using Naive Bayes: An experiment to conference paper |
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Text classification using Naive Bayes: An experiment to conference paper |
title_sort |
text classification using naive bayes: an experiment to conference paper |
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2005 |
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http://repo.uum.edu.my/12432/1/ICOQSIA_shamrie.pdf http://repo.uum.edu.my/12432/ http://lintas.uum.edu.my:8080/elmu/index.jsp?module=webopac-l&action=fullDisplayRetriever.jsp&szMaterialNo=0000332840 |
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