An efficient model selection for linear discriminant function-based recursive feature elimination.
Model selection is an important issue in support vector machine-based recursive feature elimination (SVM-RFE). However, performing model selection on a linear SVM-RFE is difficult because the generalization error of SVM-RFE is hard to estimate. This paper proposes an approximation method to evaluate...
| Publicado en: | Journal of Biomedical Informatics Vol. 129 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
May2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157285342&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157285342 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: May2022 vid: 129 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 157285342 157285342 NLM35436594 157285342 10.1016/j.jbi.2022.104070 NLM35436594 157285342 ppct: 1 formats: tig: atl: An efficient model selection for linear discriminant function-based recursive feature elimination. aug: au: Ding, Xiaojian Yang, Fan Ma, Fuming affil: College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China sug: subj: Algorithms Discriminant Analysis Bioinformatics Methods ab: Model selection is an important issue in support vector machine-based recursive feature elimination (SVM-RFE). However, performing model selection on a linear SVM-RFE is difficult because the generalization error of SVM-RFE is hard to estimate. This paper proposes an approximation method to evaluate the generalization error of a linear SVM-RFE, and designs a new criterion to tune the penalty parameter C. As the computational cost of the proposed algorithm is expensive, several alpha seeding approaches are proposed to reduce the computational complexity. We show that the performance of the proposed algorithm exceeds that of the compared algorithms on bioinformatics datasets, and empirically demonstrate the computational time saving achieved by alpha seeding approaches. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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