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K-means clustering to improve the accuracy of decision tree response classification.

The use of deep generation with statistical-based surface generation merits from response utterances readily available from corpus. Representation and quality of the instance data are the foremost factors that affect classification accuracy of the statistical-based method. Thus, in classification ta...

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Main Authors: Ali, S. A., Sulaiman , N., Mustapha, Aida, Mustapha, Norwati
Format: Article
Language:English
English
Published: Asian Network for Scientific Information (ANSINET) 2009
Online Access:http://psasir.upm.edu.my/id/eprint/15392/1/K.pdf
http://psasir.upm.edu.my/id/eprint/15392/
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spelling my.upm.eprints.153922015-11-24T04:25:19Z http://psasir.upm.edu.my/id/eprint/15392/ K-means clustering to improve the accuracy of decision tree response classification. Ali, S. A. Sulaiman , N. Mustapha, Aida Mustapha, Norwati The use of deep generation with statistical-based surface generation merits from response utterances readily available from corpus. Representation and quality of the instance data are the foremost factors that affect classification accuracy of the statistical-based method. Thus, in classification task, any irrelevant or unreliable tagging of response classes represented will result in low accuracy. This study focused on improving dialogue act classification of a user utterance into a response class by clustering the semantic and pragmatic features extracted from each user utterance. A Decision tree approach is used to classify 64 mixed-initiative, transaction dialogue corpus in theater domain. The experiment shows that by using clustering technique in pre-processing stage for re-tagging response classes, the Decision tree is able to achieve 97.5% recognition accuracy in classification, better than the 81.95% recognition accuracy when using Decision tree alone. Asian Network for Scientific Information (ANSINET) 2009 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/15392/1/K.pdf Ali, S. A. and Sulaiman , N. and Mustapha, Aida and Mustapha, Norwati (2009) K-means clustering to improve the accuracy of decision tree response classification. Information Technology Journal, 8 (8). pp. 1256-1262. ISSN 1812-5638 10.3923/itj.2009.1256.1262 English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description The use of deep generation with statistical-based surface generation merits from response utterances readily available from corpus. Representation and quality of the instance data are the foremost factors that affect classification accuracy of the statistical-based method. Thus, in classification task, any irrelevant or unreliable tagging of response classes represented will result in low accuracy. This study focused on improving dialogue act classification of a user utterance into a response class by clustering the semantic and pragmatic features extracted from each user utterance. A Decision tree approach is used to classify 64 mixed-initiative, transaction dialogue corpus in theater domain. The experiment shows that by using clustering technique in pre-processing stage for re-tagging response classes, the Decision tree is able to achieve 97.5% recognition accuracy in classification, better than the 81.95% recognition accuracy when using Decision tree alone.
format Article
author Ali, S. A.
Sulaiman , N.
Mustapha, Aida
Mustapha, Norwati
spellingShingle Ali, S. A.
Sulaiman , N.
Mustapha, Aida
Mustapha, Norwati
K-means clustering to improve the accuracy of decision tree response classification.
author_facet Ali, S. A.
Sulaiman , N.
Mustapha, Aida
Mustapha, Norwati
author_sort Ali, S. A.
title K-means clustering to improve the accuracy of decision tree response classification.
title_short K-means clustering to improve the accuracy of decision tree response classification.
title_full K-means clustering to improve the accuracy of decision tree response classification.
title_fullStr K-means clustering to improve the accuracy of decision tree response classification.
title_full_unstemmed K-means clustering to improve the accuracy of decision tree response classification.
title_sort k-means clustering to improve the accuracy of decision tree response classification.
publisher Asian Network for Scientific Information (ANSINET)
publishDate 2009
url http://psasir.upm.edu.my/id/eprint/15392/1/K.pdf
http://psasir.upm.edu.my/id/eprint/15392/
_version_ 1643825920955383808
score 13.211869

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