Associative classification framework for cancer microarray data
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 pre...
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| Format: | Article |
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American Scientific Publishers
2017
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| Online Access: | http://eprints.uthm.edu.my/3684/ https://doi.org/10.1166/asl.2017.8312 |
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| _version_ | 1833417402812989440 |
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| author | Fang, Ong Huey Mustapha, Norwati Mustapha, Aida Hamdan, Hazlina Rosli, Rozita |
| author_facet | Fang, Ong Huey Mustapha, Norwati Mustapha, Aida Hamdan, Hazlina Rosli, Rozita |
| author_sort | Fang, Ong Huey |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.uthm.eprints-3684 |
| institution | Universiti Tun Hussein Onn Malaysia |
| publishDate | 2017 |
| publisher | American Scientific Publishers |
| record_format | eprints |
| spelling | my.uthm.eprints-36842021-11-21T07:10:20Z http://eprints.uthm.edu.my/3684/ Associative classification framework for cancer microarray data Fang, Ong Huey Mustapha, Norwati Mustapha, Aida Hamdan, Hazlina Rosli, Rozita QA76 Computer software 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. American Scientific Publishers 2017-05 Article PeerReviewed 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 https://doi.org/10.1166/asl.2017.8312 |
| spellingShingle | QA76 Computer software Fang, Ong Huey Mustapha, Norwati Mustapha, Aida Hamdan, Hazlina Rosli, Rozita Associative classification framework for cancer microarray data |
| title | Associative classification framework for cancer microarray data |
| title_full | Associative classification framework for cancer microarray data |
| title_fullStr | Associative classification framework for cancer microarray data |
| title_full_unstemmed | Associative classification framework for cancer microarray data |
| title_short | Associative classification framework for cancer microarray data |
| title_sort | associative classification framework for cancer microarray data |
| topic | QA76 Computer software |
| url | http://eprints.uthm.edu.my/3684/ https://doi.org/10.1166/asl.2017.8312 |
| url_provider | http://eprints.uthm.edu.my/ |