Variable Selection in Proteomic Profile Classification by Interval Support Vector Machines (iSVM)
2014 ◽
Vol 556-562
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pp. 347-350
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For variable selection in proteomic profile classification, we present a new local modeling procedure called interval support vector machine (iSVM). This procedure builds a series of SVM models in a window that moves over the whole spectral region and then locates useful spectral intervals in terms of the least complexity of SVM models reaching a desired error level. We applied iSVM in variable selection for proteomic profile classification. The results show that the proposed procedure are very promising for classification target-based variable selection and obtain much better classification than full-spectrum SVM model.
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2007 ◽
Vol 122
(1)
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pp. 259-268
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2011 ◽
Vol 4
(1)
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pp. 19-26
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2015 ◽
Vol 78
(1)
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pp. 53-76
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2009 ◽
Vol 38
(8)
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pp. 1640-1658
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2018 ◽
Vol 1
(1)
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pp. 120-130
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