Gene Selection for Cancer Classification using a New Hybrid of Binary Black Hole Algorithm
| dc.authorid | PASHAEI, ELNAZ/0000-0001-9391-9785 | |
| dc.authorid | Pashaei, Elham/0000-0001-7401-4964 | |
| dc.contributor.author | Pashaei, Elnaz | |
| dc.contributor.author | Pashaei, Elham | |
| dc.date.accessioned | 2024-09-11T19:51:57Z | |
| dc.date.available | 2024-09-11T19:51:57Z | |
| dc.date.issued | 2020 | |
| dc.department | İstanbul Gelişim Üniversitesi | en_US |
| dc.description | 28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK | en_US |
| dc.description.abstract | This paper proposes a new hybrid approach for solving gene selection problems in cancer microarray data, which is one of the most challenging tasks in bioinformatics. Minimum-redundancy-maximum-relevance (mRMR) filter approach is combined with the binary black hole optimization algorithm (BBHA) to pick out extremely discriminative genes from cancer datasets. The support vector machine (SVM) is employed as a fitness function to accurately diagnose cancer. The experimental results prove that the suggested method exhibits better classification accuracy with the smallest gene subset compared to existing state-of-art methods. | en_US |
| dc.description.sponsorship | Istanbul Medipol Univ | en_US |
| dc.identifier.doi | 10.1109/siu49456.2020.9302351 | |
| dc.identifier.isbn | 978-1-7281-7206-4 | |
| dc.identifier.issn | 2165-0608 | |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.uri | https://doi.org/10.1109/siu49456.2020.9302351 | |
| dc.identifier.uri | https://hdl.handle.net/11363/7874 | |
| dc.identifier.wos | WOS:000653136100324 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | 2020 28th Signal Processing And Communications Applications Conference (Siu) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.snmz | 20240903_G | en_US |
| dc.subject | gene selection | en_US |
| dc.subject | binary black hole algorithm | en_US |
| dc.subject | minimal-redundancy-maximal-relevance | en_US |
| dc.subject | support vector machine | en_US |
| dc.title | Gene Selection for Cancer Classification using a New Hybrid of Binary Black Hole Algorithm | en_US |
| dc.type | Conference Object | en_US |
Dosyalar
Orijinal paket
1 - 1 / 1
