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《Acta Electronica Sinica》 2008-08
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Study on a GA-based SVM Decision-tree Multi-Classification Strategy

LIAN Ke,HUANG Jian-guo,WANG Hou-jun,LONG Bing(Department of Automation Engineering,University of Electronic Science and Technology of China,Chengdu,Sichuan 610054,China)  
This paper presents a GA-based decision-tree algorithm to deal with SVM multi-class classification problem.First,GA is used to create optimal or near-optimal decision-tree automatically,which makes the margin between two classes maximal at every decision node.Then at every decision node,standard SVM is used to make binary classification.Finally,the SVM decision tree achieves multi-classification.Theoretical analysis and experiments show that the proposed method is more precise than the traditional "DT-SVM" and "DAG-SVM" methods,and has higher training and testing efficient than classical "1-a-1" and "1-a-r" methods.
【Fund】: 国防基础科研(No.A1420061264);; 国家自然科学基金(No.60673011)
【CateGory Index】: TP18
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