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《Systems Engineering-Theory & Practice》 2012-04
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Random forest based potential k nearest neighbor classifier and its application in gene expression data

YANG Fan~1,LIN Chen~2,ZHOU Qi-feng~1,FU Chang-hong~1,LUO Lin-kai~1 (1.Department of Automation,Xiamen University,Xiamen 361005,China; 2.Department of Computer Science,Xiamen University,Xiamen 361005,China)  
Random forests(RF) has been widely used in bioinformatics especially in cancer diagnosis. This paper studies the classification scheme of RF from the viewpoint of adaptive k nearest neighbors, analyzes the information loss in RF,and proposes a new voting method called RF-based potential nearest neighbor which can use the information of OOB samples in each tree and show significant improvement. Comparison result on 6 cancer gene expression datasets demonstrated that RF-PN got better predictive accuracy than RF.
【Fund】: 国家自然科学基金(60975052);; 中央高校基本科研业务费专项资金(2010121065)
【CateGory Index】: TP311.13;TP274
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1 LIU Ye-qing~(1,2),LIU San-yang~1,GU Ming-tao~3 (1.Department of Mathematical Sciences,Xidian University,Xi'an 710071,China;2.School of Science,Henan University of Science & Technology,Luoyang 471003,China;3.PLA Unit 96251,Luoyang 471003,China);Polynomial smooth semi-supervised support vector classifier[J];Systems Engineering-Theory & Practice;2009-07
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