DC Field | Value | Language |
---|---|---|
dc.contributor.author | Seo, Hyein | ko |
dc.contributor.author | Cho, Dong-Ho | ko |
dc.date.accessioned | 2020-05-28T08:20:14Z | - |
dc.date.available | 2020-05-28T08:20:14Z | - |
dc.date.created | 2020-05-25 | - |
dc.date.created | 2020-05-25 | - |
dc.date.created | 2020-05-25 | - |
dc.date.issued | 2020-04 | - |
dc.identifier.citation | IEEE ACCESS, v.8, pp.64992 - 65004 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10203/274343 | - |
dc.description.abstract | Gene expression profiling is a useful technique for analyzing cellular function, and gene expression profiles are widely studied in human cancer research. Gene expression data usually consist of a very large number of features and a relatively small number of samples, and extracting a small number of important features from these data is a major challenge of gene expression-based analysis in cancer research. In this paper, we propose an embedded feature selection algorithm using boosted linear regression-based feature selection. The boosting technique is applied to derive the ensemble feature selector and improve the performance of linear regression-based feature selection. The proposed feature selection algorithm, called boosted regression-based feature selection for the multilayer perceptron (BREG-MLP), repeats the boosted feature selection process to extract the smallest feature subset while maintaining good classification performance. We apply the proposed BREG-MLP to some human cancer-related gene expression data sets for the purpose of extracting important features, and we confirm that BREG-MLP offers improved performance compared to single regression-based feature selection methods. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Cancer-Related Gene Signature Selection Based on Boosted Regression for Multilayer Perceptron | - |
dc.type | Article | - |
dc.identifier.wosid | 000530835300003 | - |
dc.identifier.scopusid | 2-s2.0-85083725242 | - |
dc.type.rims | ART | - |
dc.citation.volume | 8 | - |
dc.citation.beginningpage | 64992 | - |
dc.citation.endingpage | 65004 | - |
dc.citation.publicationname | IEEE ACCESS | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2985414 | - |
dc.contributor.localauthor | Cho, Dong-Ho | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Boosting | - |
dc.subject.keywordAuthor | feature selection | - |
dc.subject.keywordAuthor | linear regression | - |
dc.subject.keywordAuthor | gene expression profile | - |
dc.subject.keywordPlus | EXPRESSION SIGNATURES | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | REPRESENTATION | - |
dc.subject.keywordPlus | ALGORITHMS | - |
dc.subject.keywordPlus | PROFILES | - |
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