多重代价多粒度决策粗糙集模型研究
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摘要
决策粗糙集和多粒度粗糙集是两种重要的数据处理机制。在对多重代价决策粗糙集模型和多粒度粗糙集模型的研究基础上,通过综合考虑多重代价矩阵和多粒度思想,将权重均值代价策略引入决策粗糙集模型中,提出了一种基于权重多重代价的多粒度决策粗糙集模型。在不完备信息系统中,分析了悲观代价决策粗糙集、乐观代价决策粗糙集和权重多重代价多粒度决策粗糙集模型,并给出了以上各种模型的决策代价总代价计算公式。以权重多重代价悲观多粒度决策粗糙集模型为例,讨论了该模型下随着粒度的变化其正域的变化情况,并给出了一种基于代价最小化的粒度约简方法。该模型更好地结合了决策粗糙集模型和多粒度粗糙集模型,可从多角度分析解决决策粗糙集模型中的相关问题。
Decision-theoretic rough sets and multi-granulation rough sets are two important mechanisms of data processing. On the basis of decision-theoretic rough sets based on multi-cost and multi-granulation rough sets, by considering multi-cost matrix and multi-granularity thought, this paper introduces a weighted mean-cost strategy into decision-theoretic rough set models, and proposes a multi-granulation decision-theoretic rough set model based on weighted multi-cost. In the incomplete information system, this paper discusses the pessimistic cost decision-theretic rough sets, optimistic cost decision-theoretic rough sets and weighted multi-cost multi-granulation decision-theoretic rough set models respectively, and describes the formulas of the whole decision costs for the above models. Finally,taking the pessimistic multi-granulation decision-theoretic rough set model based on weighted multi-cost for example, this paper analyzes the monotonicity of the decision positive region with respect to knowledge granularity sets,and proposes a definition of the granularity reduction based on the minimum decision cost. The model combines the decision-theoretic rough set model and multi-granulation rough set model with a more suitable method, which can solve the problems from multiple perspectives in the decision-theoretic rough set model.
Decision-theoretic rough sets and multi-granulation rough sets are two important mechanisms of data processing. On the basis of decision-theoretic rough sets based on multi-cost and multi-granulation rough sets, by considering multi-cost matrix and multi-granularity thought, this paper introduces a weighted mean-cost strategy into decision-theoretic rough set models, and proposes a multi-granulation decision-theoretic rough set model based on weighted multi-cost. In the incomplete information system, this paper discusses the pessimistic cost decision-theretic rough sets, optimistic cost decision-theoretic rough sets and weighted multi-cost multi-granulation decision-theoretic rough set models respectively, and describes the formulas of the whole decision costs for the above models. Finally,taking the pessimistic multi-granulation decision-theoretic rough set model based on weighted multi-cost for example, this paper analyzes the monotonicity of the decision positive region with respect to knowledge granularity sets,and proposes a definition of the granularity reduction based on the minimum decision cost. The model combines the decision-theoretic rough set model and multi-granulation rough set model with a more suitable method, which can solve the problems from multiple perspectives in the decision-theoretic rough set model.
引文
[1]Pawlak Z.Rough sets[J].International Journal of Computer&Information Sciences,1982,11(5):341-356.
[2]Miao Duoqian,Li Daoguo.Rough set theory,algorithm and application[M].Beijing:Science Press,2007.
[3]Yu Hong,Wang Guoyin,Yao Yiyu.Current research and future perspective on decision-theoretic rough sets[J].Chinese Journal of Computers,2015,38(8):1628-1639.
[4]Li Huaxiong,Zhou Xianzhong.Risk decision making based on decision-theoretic rough set:a three-way view decision model[J].International Journal of Computational Intelligence Systems,2011,4(1):1-11.
[5]Dou Huili,Yang Xibei,Song Xiaoning,et al.Decision-theoretic rough set:a multicost strategy[J].Knowledge-Based Systems,2016,91:71-83.
[6]Ma Xingbin,Ju Hengrong,Yang Xibei,et al.Multi-cost based decision-theoretic rough sets in incomplete information systems[J].Journal of Nanjing University:Natural Sciences,2015,51(2):335-342.
[7]Yang Xiaoping,Yao Jingtao.Modelling multi-agent threeway decisions with decision-theoretic rough sets[J].Fundamenta Informaticae,2012,115(2):157-171.
[8]Meng Chao,Yu Jiankun.Merging three-way decisions with decision-theoretic rough sets[J].Computer Systems&Applications,2016,25(4):175-179.
[9]Yao Yiyu.Decision-theoretic rough set models[C]//LNCS4481:Proceedings of the 2nd International Conference on Rough Sets and Knowledge Technology,Toronto,May 14-16,2007.Berlin,Heidelberg:Springer,2007:1-12.
[10]Yao Yiyu,Zhao Yan.Attribute reduction in decision-theoretic rough set models[J].Information Sciences,2008,178(17):3356-3373.
[11]Jia Xiuyi,Liao Wenhe,Tang Zhenmin,et al.Minimum cost attribute reduction in decision-theoretic rough set models[J].Information Sciences,2013,219:151-167.
[12]Ju Hengrong,Yang Xibei,Yu Hualong,et al.Research on attribute reduction criteria in decision-theoretic rough set[J].Journal of Nanjing Normal University:Natural Science Edition,2015,38(1):41-47.
[13]Ma Xi.ao,Wang Guoyin,Yu Hong.Heuristic method to attribute reduction for decision region distribution preservation[J].Journal of Software,2014,25(8):1761-1780.
[14]Huang Guoshun.Positive region preservation reducts in decision-theoretic rough set models[J].Computer Engineering and Applications,2016,52(2):165-169.
[15]Qian Yuhua,Liang Jiye,Yao Yiyu,et al.MGRS:a multigranulation rough set[J].Information Sciences,2010,180(6):949-970.
[16]Zhang Ming,Cheng Ke,Yang Xibei,et al.Multigranulation rough set based on weighted granulations[J].Control and Decision,2015,30(2):222-228.
[17]Xu Yi,Li Ce.A variable precision multi-granulation rough set model based on multiple thresholds[J].Computer Engineering and Science,2016,38(8):1727-1734.
[18]Meng Huili,Ma Yuanyuan,Xu Jiuchen.Granularity reduction of variable precision pessimistic multi-granulation rough set based on granularity entropy of lower approximate distribution[J].Computer Science,2016,43(2):83-85.
[19]Yao Yiyu,She Yanhong.Rough set models in multigranulation spaces[J].Information Sciences,2016,327:40-56.
[20]Ju Hengrong,Li Huaxiong,Yang Xibei,et al.Cost-sensitive rough set:a multi-granulation approach[J].Knowledge-Based Systems,2017,123:137-153.
[21]Qian Yuhua,Zhang Hu,Sang Yanli,et al.Multigranulation decision-theoretic rough sets[J].International Journal of Approximate Reasoning,2014,55(1):225-237.
[22]Yang Hailong,Guo Zhilian.Multigranulation decision-theoretic rough sets in incomplete information systems[J].International Journal of Machine Learning and Cybernetics,2015,6(6):1005-1018.
[23]Xu Weihua,Guo Yanting.Generalized multigranulation doublequantitative decision-theoretic rough set[J].Knowledge-Based Systems,2016,105:190-205.
[24]Liu Caihui,Pedrycz W,Wang Meizhi.Covering-based multigranulation decision-theoretic rough sets[J].Journal of Intelligent&Fuzzy Systems,2017,32(1):749-765.
[25]Qian Yuhua,Liang Xinyan,Lin Guoping,et al.Local multigranulation decision-theoretic rough sets[J].International Journal of Approximate Reasoning,2017,82:119-137.
[2]苗夺谦,李道国.粗糙集理论、算法与应用[M].北京:科学出版社,2007.
[3]于洪,王国胤,姚一豫,等.决策粗糙集理论研究现状与展望[J].计算机学报,2015,38(8):1628-1639.
[6]马兴斌,鞠恒荣,杨习贝,等.不完备信息系统中的多重代价决策粗糙集[J].南京大学学报:自然科学版,2015,51(2):335-342.
[8]孟超,余建坤.三支决策与决策粗糙集融合模型[J].计算机系统应用,2016,25(4):174-179.
[12]鞠恒荣,杨习贝,于化龙,等.决策粗糙集的属性约简准则研究[J].南京师大学报:自然科学版,2015,38(1):41-47.
[13]马希骜,王国胤,于洪.决策域分布保持的启发式属性约简方法[J].软件学报,2014,25(8):1761-1780.
[14]黄国顺.保正域的决策粗糙集属性约简[J].计算机工程与应用,2016,52(2):165-169.
[16]张明,程科,杨习贝,等.基于加权粒度的多粒度粗糙集[J].控制与决策,2015,30(2):222-228.
[17]徐怡,李策.基于多重阈值的变精度多粒度粗糙集模型[J].计算机工程与科学,2016,38(8):1727-1734.
[18]孟慧丽,马媛媛,徐久成.基于下近似分布粒度熵的悲观多粒度粗糙集约简[J].计算机科学,2016,43(2):83-85.
[2]Miao Duoqian,Li Daoguo.Rough set theory,algorithm and application[M].Beijing:Science Press,2007.
[3]Yu Hong,Wang Guoyin,Yao Yiyu.Current research and future perspective on decision-theoretic rough sets[J].Chinese Journal of Computers,2015,38(8):1628-1639.
[4]Li Huaxiong,Zhou Xianzhong.Risk decision making based on decision-theoretic rough set:a three-way view decision model[J].International Journal of Computational Intelligence Systems,2011,4(1):1-11.
[5]Dou Huili,Yang Xibei,Song Xiaoning,et al.Decision-theoretic rough set:a multicost strategy[J].Knowledge-Based Systems,2016,91:71-83.
[6]Ma Xingbin,Ju Hengrong,Yang Xibei,et al.Multi-cost based decision-theoretic rough sets in incomplete information systems[J].Journal of Nanjing University:Natural Sciences,2015,51(2):335-342.
[7]Yang Xiaoping,Yao Jingtao.Modelling multi-agent threeway decisions with decision-theoretic rough sets[J].Fundamenta Informaticae,2012,115(2):157-171.
[8]Meng Chao,Yu Jiankun.Merging three-way decisions with decision-theoretic rough sets[J].Computer Systems&Applications,2016,25(4):175-179.
[9]Yao Yiyu.Decision-theoretic rough set models[C]//LNCS4481:Proceedings of the 2nd International Conference on Rough Sets and Knowledge Technology,Toronto,May 14-16,2007.Berlin,Heidelberg:Springer,2007:1-12.
[10]Yao Yiyu,Zhao Yan.Attribute reduction in decision-theoretic rough set models[J].Information Sciences,2008,178(17):3356-3373.
[11]Jia Xiuyi,Liao Wenhe,Tang Zhenmin,et al.Minimum cost attribute reduction in decision-theoretic rough set models[J].Information Sciences,2013,219:151-167.
[12]Ju Hengrong,Yang Xibei,Yu Hualong,et al.Research on attribute reduction criteria in decision-theoretic rough set[J].Journal of Nanjing Normal University:Natural Science Edition,2015,38(1):41-47.
[13]Ma Xi.ao,Wang Guoyin,Yu Hong.Heuristic method to attribute reduction for decision region distribution preservation[J].Journal of Software,2014,25(8):1761-1780.
[14]Huang Guoshun.Positive region preservation reducts in decision-theoretic rough set models[J].Computer Engineering and Applications,2016,52(2):165-169.
[15]Qian Yuhua,Liang Jiye,Yao Yiyu,et al.MGRS:a multigranulation rough set[J].Information Sciences,2010,180(6):949-970.
[16]Zhang Ming,Cheng Ke,Yang Xibei,et al.Multigranulation rough set based on weighted granulations[J].Control and Decision,2015,30(2):222-228.
[17]Xu Yi,Li Ce.A variable precision multi-granulation rough set model based on multiple thresholds[J].Computer Engineering and Science,2016,38(8):1727-1734.
[18]Meng Huili,Ma Yuanyuan,Xu Jiuchen.Granularity reduction of variable precision pessimistic multi-granulation rough set based on granularity entropy of lower approximate distribution[J].Computer Science,2016,43(2):83-85.
[19]Yao Yiyu,She Yanhong.Rough set models in multigranulation spaces[J].Information Sciences,2016,327:40-56.
[20]Ju Hengrong,Li Huaxiong,Yang Xibei,et al.Cost-sensitive rough set:a multi-granulation approach[J].Knowledge-Based Systems,2017,123:137-153.
[21]Qian Yuhua,Zhang Hu,Sang Yanli,et al.Multigranulation decision-theoretic rough sets[J].International Journal of Approximate Reasoning,2014,55(1):225-237.
[22]Yang Hailong,Guo Zhilian.Multigranulation decision-theoretic rough sets in incomplete information systems[J].International Journal of Machine Learning and Cybernetics,2015,6(6):1005-1018.
[23]Xu Weihua,Guo Yanting.Generalized multigranulation doublequantitative decision-theoretic rough set[J].Knowledge-Based Systems,2016,105:190-205.
[24]Liu Caihui,Pedrycz W,Wang Meizhi.Covering-based multigranulation decision-theoretic rough sets[J].Journal of Intelligent&Fuzzy Systems,2017,32(1):749-765.
[25]Qian Yuhua,Liang Xinyan,Lin Guoping,et al.Local multigranulation decision-theoretic rough sets[J].International Journal of Approximate Reasoning,2017,82:119-137.
[2]苗夺谦,李道国.粗糙集理论、算法与应用[M].北京:科学出版社,2007.
[3]于洪,王国胤,姚一豫,等.决策粗糙集理论研究现状与展望[J].计算机学报,2015,38(8):1628-1639.
[6]马兴斌,鞠恒荣,杨习贝,等.不完备信息系统中的多重代价决策粗糙集[J].南京大学学报:自然科学版,2015,51(2):335-342.
[8]孟超,余建坤.三支决策与决策粗糙集融合模型[J].计算机系统应用,2016,25(4):174-179.
[12]鞠恒荣,杨习贝,于化龙,等.决策粗糙集的属性约简准则研究[J].南京师大学报:自然科学版,2015,38(1):41-47.
[13]马希骜,王国胤,于洪.决策域分布保持的启发式属性约简方法[J].软件学报,2014,25(8):1761-1780.
[14]黄国顺.保正域的决策粗糙集属性约简[J].计算机工程与应用,2016,52(2):165-169.
[16]张明,程科,杨习贝,等.基于加权粒度的多粒度粗糙集[J].控制与决策,2015,30(2):222-228.
[17]徐怡,李策.基于多重阈值的变精度多粒度粗糙集模型[J].计算机工程与科学,2016,38(8):1727-1734.
[18]孟慧丽,马媛媛,徐久成.基于下近似分布粒度熵的悲观多粒度粗糙集约简[J].计算机科学,2016,43(2):83-85.