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


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

Abstract / Synopsis

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.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number:
Publisher: Asian Network for Scientific Information (ANSINET)
Keywords: Classification; Clustering; K-means; Decision tree; Natural language generation; Dialogue systems.
Depositing User: Nida Hidayati Ghazali
Date Deposited: 22 Jul 2013 08:55
Last Modified: 24 Nov 2015 12:25
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