Shiba, Omar A. A. (2010) Towards a better feature subset selection approach. In: Knowledge Management International Conference 2010 (KMICe2010), 25-27 May 2010, Kuala Terengganu, Malaysia.

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Abstract

The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	ISBN 978-983-2078-40-1 Organized by: UUM College of Art & Sciences, Universiti Utara Malaysia
Uncontrolled Keywords:	Feature selection, classification accuracy, slicing, irrelevant features.
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Divisions:	College of Arts and Sciences
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	05 Jun 2014 01:06
Last Modified:	05 Jun 2014 01:06
URI:	https://repo.uum.edu.my/id/eprint/11237

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