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《Computer Engineering and Design》 2008-13
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Feature selection of deep web based on Tabu

TAN Chun-liang1,GAN Dan1,CHEN Li-na1,JIANG Yun-cheng1,2(1.College of Computer Science,Guangxi Normal University,Guilin 541004,China;2.Department of Computer Science,Sun Yat-Sen University,Guangzhou 510275,China)  
Classification of deep web has characteristic of small sample and high dimensional,which restricts choice of classification algorithm and makes the classifier hardly design,also lower the" Generalization Ability "and makes the classifier" overfitting." Feature selection is necessary to avoid"curse of dimensionality." There is no research about automatic classification algorithm at present.Feature selection algorithm of deep web based on Tabu search algorithm and separative criterion is put forward through the research about feature selection,which can quickly find feature subset in the time complexity of 2.Feature selection algorithm based on Tabu and separative criterion makes design of classifier easily,also increases accuracy of classifier.Experimentation based on classifier indicates that feature selection algorithm based on Tabu and separative criterion increases accuracy of classifier and reduces computation complexity.
【Fund】: 国家自然科学基金项目(60663001);; 中国博士后科学基金项目(20060400226);; 广西青年科学基金项目(桂科青0640030)
【CateGory Index】: TP391.1
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