Fang, Ong Huey 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 1936-6612
Full text not available from this repository. (Request a copy)Abstract
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 Applied Science and Technology > Department of Mathematics and Statistics |
| Depositing User: | Miss Nur Rasyidah Rosli |
| Date Deposited: | 21 Nov 2021 07:10 |
| Last Modified: | 21 Nov 2021 07:10 |
| URI: | http://eprints.uthm.edu.my/id/eprint/3684 |
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