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Associative classification framework for cancer microarray data

Ong , Huey Fang and Mustapha, Norwati and Mustapha, Aida and Hamdan, Hazlina and Rosli, Rozita (2017) Associative classification framework for cancer microarray data. Advanced Science Letters, 23 (5). pp. 4153-4157. ISSN 19366612

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Having good cancer classifiers are crucial in order to give the most effective and cost saving treatments for patients. Microarray is one of the vital tools in cancer studies, as it allows the discovery of gene expression patterns and promises better accuracy of cancer classification. This paper presents an associative classification framework for microarray data. The proposed framework combined the strength of both filter method and association rule mining. The experimental results showed that the selected gene subsets from generated association rules can improve the accuracy and interpretability of classifiers.

Item Type: Article
Uncontrolled Keywords: Association rule mining; associative classification; gene expression; information gain; microarray
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 30 Apr 2019 01:04
Last Modified: 30 Apr 2019 01:04
URI: http://eprints.uthm.edu.my/id/eprint/10937
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