一种基于相对信息粒度的属性约简算法
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摘要
为了有效寻找决策信息系统中的约简,借鉴已有约简算法,依托粒计算理论,论文用信息粒库从语义和语法两个方面来刻画知识,利用相对信息粒度来衡量属性集的粒化能力,并据此提出一种基于相对信息粒度的属性约简算法,最终通过实例分析验证了该算法的有效性。
In order to find reduction in decision information system,on the basis of the existent reduction algorithm and granular computing theory,this paper depicts knowledge by using information granular base from two aspects:semantic and grammar.Then,attribute sets' granulating ability is measured by relative information granularity and this paper gives an attribute reduction algorithm based on relative information granularity. Finally,experimental results verify the effectiveness of this algorithm.
In order to find reduction in decision information system,on the basis of the existent reduction algorithm and granular computing theory,this paper depicts knowledge by using information granular base from two aspects:semantic and grammar.Then,attribute sets' granulating ability is measured by relative information granularity and this paper gives an attribute reduction algorithm based on relative information granularity. Finally,experimental results verify the effectiveness of this algorithm.
引文
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[4]张宁,范年柏.基于邻域近似条件熵的启发式属性约简[J].计算机应用研究,2018,35(05):1395-1398.ZHANG Ning,FAN Nianbai. Heuristic attribute reduction based on neighborhood approximate conditional entropy[J]. Application research of computers,2018,35(05):1395-1398.
[5]李华,江峰,于旭,等.基于粒度决策熵的属性约简[J].计算机与现代化,2018(04):7-12.LI Hua,JIANG Feng,YU Xu,et al. Attribute reduction based on granularity decision entropy[J]. Computer and Modernization,2018(04):7-12.
[6]续欣莹,张扩,谢珺,等.基于互信息下粒子群优化的属性约简算法[J].电子学报,2017,45(11):2695-2704.XU Xinying,ZHANG Kuo,XIE Gang,et al. An Attribute Reduction Algorithm based on Mutual Information of Particle Swarm Optimization[J]. acta electronica sinica,2017,45(11):2695-2704.
[7]丁棉卫,张腾飞,马福民.基于二进制区分矩阵的不完备系统增量式属性约简算法[J].计算机科学,2017,44(07):244-250.DING Mianwei,ZHANG Tengfei,MA Fumin. Incremental Attribute Reduction Algorithm based on binary Discernibility Matrix[J]. Computer Science,2017,44(07):244-250.
[8]王治和,崔晓慧.改进的差别矩阵启发式属性约简算法[J].计算机工程与设计,2016,37(4):1032-1036.WANG Zhihe,CUI Xiaohui. Improved difference matrix heuristic attribute reduction algorithm[J]. Computer Engineering and Design,2016,37(4):1032-1036.
[9]冀素琴,石洪波,吕亚丽.基于粒计算与区分能力的属性约简算法[J].模式识别与人工智能,2015,28(04):327-334.JI Suqin,SHI Hongbo,LV Yali. An Attribute Reduction Algorithm Based on Granular Computing and Discernibility[J]. Pattern Recognition and Artificial Intelligence,2015,28(04):327-334.
[10]G. Chiaselotti,T. Gentile,F. Infusino. Knowledge Pairing Systems in Granular Computing[J]. Knowledge-Based Systems,2017:231-237.
[11]郑鹭斌,陈玉明,曾志强,等.二进制粒计算模型[J].计算机科学,2016,43(01):270-274.ZHENG Lubin,CHEN Yuming,ZENG Zhiqiang,et al.Binary Granular Computing Model[J]. Computer Science,2016,43(01):270-274.
[12]唐孝,舒兰.基于粒计算的属性约简改进算法[J].计算机科学,2014,41(S2):313-315.TANG Xiao,SHU Lan. Improved Algorithm of Attribute Reduction Based on Granular Computing[J]. Computer Science,2014,41(S2):313-315.
[13]乔丽娟,徐章艳,谢小军,等.基于知识粒度的不完备决策表的属性约简算法[J].智能系统学报,2016,11(1):129-135.QIAO Lijuan,XU Zhangyan,XIE Xiaojun,et al.Efficient attribute reduction algorithm for an incomplete decision table based on knowledge granulation[J]. CAAI Transactions on Intelligent Systems,2016,11(1):129-135.
[14]常红岩,蒙祖强.一种新的决策粗糙集启发式属性约简算法[J].计算机科学,2016,43(6):218-222.CHANG Hongyan,MENG Zuqiang. New Heuristic Algorithm for Attribute Reduction in Decision-theoretic Rough Set[J]. Computer Science,2016,43(6):218-222.
[15]吴尚智,罗艺纯,翟敬鹏.基于遗传粒子群和粗糙集的最小属性约简算法[J].计算机工程与科学,2016,38(5):1007-1013.WU Shangzhi,LUO Yichun,ZHAI Jingpeng. A minimum attribute reduction algorithm based on genetic&particle swarm optimization and rough set[J]. Computer Engineering&Science,2016,38(5):1007-1013.
[16]唐坤剑,容强.基于加权浓缩树的粗糙集属性约简算法[J].计算机工程与应用,2018(2):76-81.TANG Kunjian,RONG Qiang. Attribute reduction algorithm based on weighted condensed tree. Computer Engineering and Applications,2018,54(2):76-81.
[2]苗夺谦,王国胤,刘清,等.粒计算:过去、现在与展望[M].北京:科学出版社,2007:55-67.MIAO Duqian,WANG Guoyin,LIU Qing,et al. Granular Computing:Past,Now and Future[M]. Beijing:Science Press,2007:55-67.
[3]史博文,李国和,吴卫江,等.基于强化正域的属性约简方法[J].计算机应用研究,2017,34(01):107-109.SHI Bowen,LI Guohe,WU Weijiang,et al. Attribute reduction method based on enhanced positive region[J].Application research of computers,2017,34(01):107-109.
[4]张宁,范年柏.基于邻域近似条件熵的启发式属性约简[J].计算机应用研究,2018,35(05):1395-1398.ZHANG Ning,FAN Nianbai. Heuristic attribute reduction based on neighborhood approximate conditional entropy[J]. Application research of computers,2018,35(05):1395-1398.
[5]李华,江峰,于旭,等.基于粒度决策熵的属性约简[J].计算机与现代化,2018(04):7-12.LI Hua,JIANG Feng,YU Xu,et al. Attribute reduction based on granularity decision entropy[J]. Computer and Modernization,2018(04):7-12.
[6]续欣莹,张扩,谢珺,等.基于互信息下粒子群优化的属性约简算法[J].电子学报,2017,45(11):2695-2704.XU Xinying,ZHANG Kuo,XIE Gang,et al. An Attribute Reduction Algorithm based on Mutual Information of Particle Swarm Optimization[J]. acta electronica sinica,2017,45(11):2695-2704.
[7]丁棉卫,张腾飞,马福民.基于二进制区分矩阵的不完备系统增量式属性约简算法[J].计算机科学,2017,44(07):244-250.DING Mianwei,ZHANG Tengfei,MA Fumin. Incremental Attribute Reduction Algorithm based on binary Discernibility Matrix[J]. Computer Science,2017,44(07):244-250.
[8]王治和,崔晓慧.改进的差别矩阵启发式属性约简算法[J].计算机工程与设计,2016,37(4):1032-1036.WANG Zhihe,CUI Xiaohui. Improved difference matrix heuristic attribute reduction algorithm[J]. Computer Engineering and Design,2016,37(4):1032-1036.
[9]冀素琴,石洪波,吕亚丽.基于粒计算与区分能力的属性约简算法[J].模式识别与人工智能,2015,28(04):327-334.JI Suqin,SHI Hongbo,LV Yali. An Attribute Reduction Algorithm Based on Granular Computing and Discernibility[J]. Pattern Recognition and Artificial Intelligence,2015,28(04):327-334.
[10]G. Chiaselotti,T. Gentile,F. Infusino. Knowledge Pairing Systems in Granular Computing[J]. Knowledge-Based Systems,2017:231-237.
[11]郑鹭斌,陈玉明,曾志强,等.二进制粒计算模型[J].计算机科学,2016,43(01):270-274.ZHENG Lubin,CHEN Yuming,ZENG Zhiqiang,et al.Binary Granular Computing Model[J]. Computer Science,2016,43(01):270-274.
[12]唐孝,舒兰.基于粒计算的属性约简改进算法[J].计算机科学,2014,41(S2):313-315.TANG Xiao,SHU Lan. Improved Algorithm of Attribute Reduction Based on Granular Computing[J]. Computer Science,2014,41(S2):313-315.
[13]乔丽娟,徐章艳,谢小军,等.基于知识粒度的不完备决策表的属性约简算法[J].智能系统学报,2016,11(1):129-135.QIAO Lijuan,XU Zhangyan,XIE Xiaojun,et al.Efficient attribute reduction algorithm for an incomplete decision table based on knowledge granulation[J]. CAAI Transactions on Intelligent Systems,2016,11(1):129-135.
[14]常红岩,蒙祖强.一种新的决策粗糙集启发式属性约简算法[J].计算机科学,2016,43(6):218-222.CHANG Hongyan,MENG Zuqiang. New Heuristic Algorithm for Attribute Reduction in Decision-theoretic Rough Set[J]. Computer Science,2016,43(6):218-222.
[15]吴尚智,罗艺纯,翟敬鹏.基于遗传粒子群和粗糙集的最小属性约简算法[J].计算机工程与科学,2016,38(5):1007-1013.WU Shangzhi,LUO Yichun,ZHAI Jingpeng. A minimum attribute reduction algorithm based on genetic&particle swarm optimization and rough set[J]. Computer Engineering&Science,2016,38(5):1007-1013.
[16]唐坤剑,容强.基于加权浓缩树的粗糙集属性约简算法[J].计算机工程与应用,2018(2):76-81.TANG Kunjian,RONG Qiang. Attribute reduction algorithm based on weighted condensed tree. Computer Engineering and Applications,2018,54(2):76-81.