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《Journal of Tianjin Normal University(Natural Science Edition)》 2018-02
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Feature selection method based on measure of subproblem classification ability

LIU Lei;ZHENG Taoran;ZHAO Chenfei;LIU Lin;WANG Shuqin;HE Maowei;College of Computer and Information Engineering,Tianjin Normal University;School of computer Science and Software Engineering,Tianjin Polytechnic University;  
Feature selection is one of the basic problems in the field of machine learning,especially is important and nec-essary for processing large scale data. Most of existing feature selection methods generally compute only one discriminant value with respect to class variable for a feature to indicate its classfication ability. Aiming this problem,a feature selection method based on subproblem classification ability is proposed. The method uses the each subproblems classification ability of feature and their weighted average to measure the classification ability of each feature,which can not only ensure that the features with strong classification ability are selected,but also that the features with weak classification ability but strong subproblem classification ability are selected. The proposed method is compared with 3 related methods for feature selection on 4 open gene expression datasets. Experimental results demonstrate the effectiveness of the proposed method,and the classification and prediction accuracy rate are improved.
【Fund】: 国家自然科学基金资助项目(61070089);; 天津市应用基础与前沿技术研究计划重点资助项目(15JCYBJC4600);; 天津市科技计划资助项目(16ZLZDZF00150)
【CateGory Index】: TP181
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