%0 Journal Article
	%A Yasmin Mohd Yacob and  Harsa A. Mat Sakim and  Nor Ashidi Mat Isa
	%D 2012
	%J International Journal of Mathematical and Computational Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 62, 2012
	%T Decision Tree-based Feature Ranking using Manhattan Hierarchical Cluster Criterion
	%U https://publications.waset.org/pdf/1993
	%V 62
	%X Feature selection study is gaining importance due to its contribution to save classification cost in terms of time and computation load. In search of essential features, one of the methods to search the features is via the decision tree. Decision tree act as an intermediate feature space inducer in order to choose essential features. In decision tree-based feature selection, some studies used decision tree as a feature ranker with a direct threshold measure, while others remain the decision tree but utilized pruning condition that act as a threshold mechanism to choose features. This paper proposed threshold measure using Manhattan Hierarchical Cluster distance to be utilized in feature ranking in order to choose relevant features as part of the feature selection process. The result is promising, and this method can be improved in the future by including test cases of a higher number of attributes.

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