
	Information Technology Journal
	Year: 2009 \| Volume: 8 \| Issue: 8 \| Page No.: 1256-1262 DOI: 10.3923/itj.2009.1256.1262

K-Means Clustering to Improve the Accuracy of Decision Tree Response Classification

S.A. Ali, N. Sulaiman, A. Mustapha and N. Mustapha

Abstract: 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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How to cite this article:

S.A. Ali, N. Sulaiman, A. Mustapha and N. Mustapha, 2009. K-Means Clustering to Improve the Accuracy of Decision Tree Response Classification. Information Technology Journal, 8: 1256-1262.

DOI: 10.3923/itj.2009.1256.1262

URL: https://scialert.net/abstract/?doi=itj.2009.1256.1262

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