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《Chinese Journal of Health Statistics》 2007-02
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The Stepwise Discriminant Analysis of Random Forests Used in Gene Expression Data

Wu Xiaoyan,Yan Xiaoguang,Li Kang.Department of Medical Statistics,Harbin Medical University (150001),Harbin  
ObjectiveWe promote a new arithmetic of random forests,which selects variables automatically during the model formation and establishes the optimal discriminant model.MethodsThe arithmetic chooses variables based on the values of variable importance and the stepwise iteration.The method is applied to real gene datasets and we validate its effect using R language via simulated tests.ResultsThe optimal discriminant model performs well in real gene expression data with small number of genes being selected.The simulated tests show that the higher the value of ROC area,the better results of discrimination Random Forests achieve,and the differential variables still remain in the model to promote the effect of classification.ConclusionThe stepwise discriminant analysis of Random Forests can be effectively applied to the research of gene selection and classification.Meanwhile,we should pay close attention to the impact produced by random fluctuation on the results.
【Fund】: 国家自然科学基金资助(30371253);; 黑龙江省重点项目(GB04C30202)
【CateGory Index】: R195
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1 WANG Jing XIA Jie-lai YE Dong-qing (1.Department of Epidemiology and Health Statistics,Anhui Medical University,Hefei,230032, China; 2.Department of Prevental Medicine,the Forth Military Medical University,Xi'an,710000,China);Improvement of Discriminant Analysis in the Medical Area[J];Application of Statistics and Management;2008-02
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