A comparative study of classification methods for metabolomics data(Competition 2)
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概要
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The purpose of classification for metabolomics data is finding a subset of metabolites called marker candidate which can separate groups efficiently as well as discriminating the groups. We evaluate and compare 5 classification methods on 26 real datasets, and provide the guidelines for finding marker candidate from appropriate classification method. Although this study shows that the predictive accuracies from 5 methods are sufficiently higher (more than 90%) in 19 cases among 26 datasets, PLSDA and SDA give better performance than other methods from the aspects of classification accuracy and metabolites selection.
- 2011-11-11
著者
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Lee Dongho
School Of Mechanical And Aerospace Engineering Seoul National University
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Lee Jae
Department Of Acupuncture & Moxibustion College Of Oriental Medicine
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Lee Jae
Department Of Statistics Korea University
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Lee DongHyuk
Department of Statistics, Korea University
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Lee Donghyuk
Department Of Statistics Korea University
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Lee Dongho
School Of Life Sciences And Biotechnology Korea University
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