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《Journal of Systems Science and Mathematical Sciences》 2019-06
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Attribute Selection Method Combined with MapReduce-Based Improved BACO and Fractal Dimension

XU Lifen;NI Zhiwei;ZHU Xuhui;JIA Kai;WU Zhangjun;School of Management, Hefei University of Technology;Key Laboratory of Process Optimization and Intelligent Decision-Making, Hefei University of Technology;  
Attribute selection is a primary preprocessing step for reducing dimension of datasets in data mining.When it comes to an attribute selection problem of high-dimensional data under big data environment,an attribute selection method based on an improved Binary Ant Colony Optimization Algorithm(MRIBACO)and fractal dimension is proposed.Firstly,an improved Binary Ant Colony Optimization Algorithm is proposed,combining with MapReduce programming model,by introducing an ariable parameter selection of location strategy,a cross variation strategy for partial optimization and a new pheromone updating rule with blocking mechanism.Secondly,fractal dimension is used as an evaluation standard for attribute subsets,and MRIBACO algorithm is employed as search strategy in the discrete solution space.The experimental results on 6 UCI datasets show that,compared with other algorithms,this algorithm achieves a better operation efficiency,and its effectiveness and stability are better than others.
【Fund】: 国家自然科学基金重大研究计划培育项目(91546108) 国家自然科学基金重大项目(91490725) 国家自然科学基金创新群体项目(71521001);; 安徽省自然科学基金(1908085QG298);; 国家重点研发计划(2016YFF0202604);; 中央高校基本科研业务费专项资金(JZ2019HGTA0053 JZ2019HGBZ0128)资助课题
【CateGory Index】: TP18
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