Citation
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; ESSN: 1936-7317
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.
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Additional Metadata
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Computer Science and Information Technology Faculty of Medicine and Health Science |
| DOI Number: | https://doi.org/10.1166/asl.2017.8312 |
| Publisher: | American Scientific Publishers |
| Keywords: | Association rule mining; Associative classification; Gene expression; Information gain; Microarray |
| Depositing User: | Nurul Ainie Mokhtar |
| Date Deposited: | 09 May 2019 04:41 |
| Last Modified: | 09 May 2019 04:41 |
| Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1166/asl.2017.8312 |
| URI: | http://psasir.upm.edu.my/id/eprint/46531 |
| Statistic Details: | View Download Statistic |
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