Constructing Support Vector Machine Ensembles for Cancer Classification Based on Proteomic Profiling

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Constructing Support Vector Machine Ensembles for Cancer Classification Based on Proteomic Profiling

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Title: Constructing Support Vector Machine Ensembles for Cancer Classification Based on Proteomic Profiling
Author: Mao, Yong; Zhou, Xiao-Bo; Pi, Dao-Ying; Sun, You-Xian

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Citation: Mao, Yong, Xiao-Bo Zhou, Dao-Ying Pi, and You-Xian Sun. 2005. “Constructing Support Vector Machine Ensembles for Cancer Classification Based on Proteomic Profiling.” Genomics, Proteomics & Bioinformatics 3 (4): 238-241. doi:10.1016/S1672-0229(05)03033-0. http://dx.doi.org/10.1016/S1672-0229(05)03033-0.
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Abstract: In this study, we present a constructive algorithm for training cooperative support vector machine ensembles (CSVMEs). CSVME combines ensemble architecture design with cooperative training for individual SVMs in ensembles. Unlike most previous studies on training ensembles, CSVME puts emphasis on both accuracy and collaboration among individual SVMs in an ensemble. A group of SVMs selected on the basis of recursive classifier elimination is used in CSVME, and the number of the individual SVMs selected to construct CSVME is determined by 10-fold cross-validation. This kind of SVME has been tested on two ovarian cancer datasets previously obtained by proteomic mass spectrometry. By combining several individual SVMs, the proposed method achieves better performance than the SVME of all base SVMs.
Published Version: doi:10.1016/S1672-0229(05)03033-0
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5173238/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:29738932
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