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DC Field | Value | Language |
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dc.contributor.author | Panudech Jinthanasatian | en_US |
dc.contributor.author | Sansanee Auephanwiriyakul | en_US |
dc.contributor.author | Nipon Theera-Umpon | en_US |
dc.date.accessioned | 2018-09-05T04:25:47Z | - |
dc.date.available | 2018-09-05T04:25:47Z | - |
dc.date.issued | 2018-02-02 | en_US |
dc.identifier.other | 2-s2.0-85046136496 | en_US |
dc.identifier.other | 10.1109/SSCI.2017.8280967 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85046136496&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/58511 | - |
dc.description.abstract | © 2017 IEEE. Neuro-fuzzy is one of the popular tools used in many applications including microarray classification. In this paper, we introduce a neuro-fuzzy with firefly algorithm with its application to microarray classification. Our neuro-fuzzy is able to select good feature sets and generate rule sets as classifier. We compare our results on seven public data sets, i.e., Lung cancer, Ovarian cancer, Prostate cancer, Leukemia (ALL/AML), Breast cancer, Colon cancer, and Diffuse large B-cell lymphoma (DLBCL), with the results from the existing algorithms. We found that our algorithm can provide comparable results with smaller numbers of selected features. However, our algorithm can provide more understandable rule sets to human than other existing algorithms. | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Mathematics | en_US |
dc.title | Microarray data classification using neuro-fuzzy classifier with firefly algorithm | en_US |
dc.type | Conference Proceeding | en_US |
article.title.sourcetitle | 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings | en_US |
article.volume | 2018-January | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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