Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58511
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dc.contributor.authorPanudech Jinthanasatianen_US
dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.contributor.authorNipon Theera-Umponen_US
dc.date.accessioned2018-09-05T04:25:47Z-
dc.date.available2018-09-05T04:25:47Z-
dc.date.issued2018-02-02en_US
dc.identifier.other2-s2.0-85046136496en_US
dc.identifier.other10.1109/SSCI.2017.8280967en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85046136496&origin=inwarden_US
dc.identifier.urihttp://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.subjectComputer Scienceen_US
dc.subjectMathematicsen_US
dc.titleMicroarray data classification using neuro-fuzzy classifier with firefly algorithmen_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedingsen_US
article.volume2018-Januaryen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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