不完备邻域多粒度决策理论粗糙集与三支决策
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摘要
多粒度决策理论粗糙集是多粒度视角下三支决策中一种重要的模型。在数值型不完备数据下建立邻域容差关系;在其基础上提出乐观和悲观的邻域多粒度决策理论粗糙集模型。为了弥补这两种模型的局限,提出平均邻域多粒度决策理论粗糙集模型,并分析相关性质以及相互关系。同时为了使所提出的邻域多粒度决策理论粗糙集适用于不完备数据环境,运用区间值的形式表示代价函数,并通过选取不同参数的方式提出一种可变三支决策。实例分析表明,该模型与方法具有一定的合理性与灵活性。
Multi-granulation decision-theoretic rough set is an important model in three-way decision under multi-granulation perspective. The neighborhood tolerance relation was established under the numerical incomplete data, and we proposed optimistic and pessimistic neighborhood multi-granulation decision-theoretic rough set model. To make up for these two models' limitation, we further proposed the mean neighborhood multi-granulation decision-theoretic rough set model, and analyzed the related properties and relations. In order to make the proposed model suitable for incomplete data environment, we used interval form to express cost functions, and proposed a variable three-way decision by selecting different parameters. The example analysis shows that the model and method have certain rationality and flexibility.
Multi-granulation decision-theoretic rough set is an important model in three-way decision under multi-granulation perspective. The neighborhood tolerance relation was established under the numerical incomplete data, and we proposed optimistic and pessimistic neighborhood multi-granulation decision-theoretic rough set model. To make up for these two models' limitation, we further proposed the mean neighborhood multi-granulation decision-theoretic rough set model, and analyzed the related properties and relations. In order to make the proposed model suitable for incomplete data environment, we used interval form to express cost functions, and proposed a variable three-way decision by selecting different parameters. The example analysis shows that the model and method have certain rationality and flexibility.
引文
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[15] 张越兵,苗夺谦,张志飞.基于三支决策的多粒度文本情感分类模型[J].计算机科学,2017,44(12):188-215.
[16] Zhang H R,Min F.Three-way recommender systems based on random forests[J].Knowledge-Based Systems,2016,91:275-286.
[17] Xu J F,Miao D Q,Zhang Y J,et al.A three-way decisions model with probabilistic rough sets for stream computing[J].International Journal of Approximate Reasoning,2017,88:1-22.
[18] Zhong C F,Pedrycz W,Wang D,et al.Granular data imputation:A framework of Granular Computing[J].Applied Soft Computing,2016,46:307-316.
[19] Qian Y H,Liang J Y,Yao Y Y,et al.MGRS:A multi-granulation rough set[J].Information Sciences,2010,180(6):949-970.
[20] Qian Y H,Zhang H,Sang Y L,et al.Multigranulation decision-theoretic rough sets[J].International Journal of Approximate Reasoning,2014,55(1):225-237.
[21] Li W W,Huang Z Q,Jia X Y,et al.Neighborhood based decision-theoretic rough set models[J].International Journal of Approximate Reasoning,2016,69:1-17.
[22] Yuan J L,Chen M C,Jiang T,et al.Complete tolerance relation based parallel filling for incomplete energy big data[J].Knowledge-Based Systems,2017,132(15):215-225.
[23] 张明,唐振民,徐维艳,等.可变多粒度粗糙集模型[J].模式识别与人工智能,2012,25(4):709-720.
[24] Dai J H,Xu Q.Approximations and uncertainty measures in incomplete information systems[J].Information Sciences,2012,98(1):62-80.
[25] Liu D,Liang D C,Wang C C.A novel three-way decision model based on incomplete information system[J].Knowledge-Based Systems,2016,91:32-45.
[26] Lin G P,Qian Y H,Li J J.NMGRS:Neighborhood-based multigranulation rough sets[J].International Journal of Approximate Reasoning,2012,53(7):1080-1093.
[27] Zhao H,Qin K Y.Mixed feature selection in incomplete decision table[J].Knowledge-Based Systems,2014,57:181-190.
[2] 张清华,薛玉斌,胡峰,等.粗糙集近似集不确定性研究[J].电子学报,2016,44(7):1574-1580.
[3] Li F C,Yang J N,Jin C X,et al.A new effect-based roughness measure for attribute reduction in information system[J].Information Sciences,2017,378:348-362.
[4] Zhang X,Mei C L,Chen D G,et al.A fuzzy rough set-based feature selection method using representative instances[J].Knowledge-Based Systems,2018,151(1):216-229.
[5] Yao Y Y.The superiority of three-way decisions in probabilistic rough set models[J].Information Sciences,2011,181(6):1080-1096.
[6] Yao Y Y.Two semantic issues in a probabilistic rough set model[J].Fundamenta Informaticae,2011,108:249-265.
[7] Kang X P,Miao D Q.A variable precision rough set model based on the granularity of tolerance relation[J].Knowledge-Based Systems,2016,102(15):103-115.
[8] 姚晟,徐风,赵鹏,等.基于邻域量化容差关系粗糙集模型的特征选择算法[J].模式识别与人工智能,2017,30(5):416-428.
[9] Hu Q H,Yu D R,Liu J F,et al.Neighborhood rough set based heterogeneous feature subset selection[J].Information Sciences,2008,178(18):3577-3594.
[10] An S,Hu Q H,Pedrycz W,et al.Data-Distribution-Aware fuzzy rough set model and its application to robust classification[J].IEEE Transactions on Cybernetics,2015,46(12):3073-3085.
[11] Ma J M,Zou C J,Pan X C.Structured probabilistic rough set approximations[J].International Journal of Approximate Reasoning,2017,90:319-332.
[12] Zhang Q H,Zhang Q,Wang G Y.The uncertainty of probabilistic rough sets in multi-granulation spaces[J].International Journal of Approximate Reasoning,2016,77:38-54.
[13] Yao Y Y,Wong S K.A decision theoretic framework for approximating concepts[J].International Journal of Man-Machine Studies,1992,37(6):793-809.
[14] Yao Y Y.Three-way decisions with probabilistic rough sets[J].Information Sciences,2010,180(3):341-353.
[15] 张越兵,苗夺谦,张志飞.基于三支决策的多粒度文本情感分类模型[J].计算机科学,2017,44(12):188-215.
[16] Zhang H R,Min F.Three-way recommender systems based on random forests[J].Knowledge-Based Systems,2016,91:275-286.
[17] Xu J F,Miao D Q,Zhang Y J,et al.A three-way decisions model with probabilistic rough sets for stream computing[J].International Journal of Approximate Reasoning,2017,88:1-22.
[18] Zhong C F,Pedrycz W,Wang D,et al.Granular data imputation:A framework of Granular Computing[J].Applied Soft Computing,2016,46:307-316.
[19] Qian Y H,Liang J Y,Yao Y Y,et al.MGRS:A multi-granulation rough set[J].Information Sciences,2010,180(6):949-970.
[20] Qian Y H,Zhang H,Sang Y L,et al.Multigranulation decision-theoretic rough sets[J].International Journal of Approximate Reasoning,2014,55(1):225-237.
[21] Li W W,Huang Z Q,Jia X Y,et al.Neighborhood based decision-theoretic rough set models[J].International Journal of Approximate Reasoning,2016,69:1-17.
[22] Yuan J L,Chen M C,Jiang T,et al.Complete tolerance relation based parallel filling for incomplete energy big data[J].Knowledge-Based Systems,2017,132(15):215-225.
[23] 张明,唐振民,徐维艳,等.可变多粒度粗糙集模型[J].模式识别与人工智能,2012,25(4):709-720.
[24] Dai J H,Xu Q.Approximations and uncertainty measures in incomplete information systems[J].Information Sciences,2012,98(1):62-80.
[25] Liu D,Liang D C,Wang C C.A novel three-way decision model based on incomplete information system[J].Knowledge-Based Systems,2016,91:32-45.
[26] Lin G P,Qian Y H,Li J J.NMGRS:Neighborhood-based multigranulation rough sets[J].International Journal of Approximate Reasoning,2012,53(7):1080-1093.
[27] Zhao H,Qin K Y.Mixed feature selection in incomplete decision table[J].Knowledge-Based Systems,2014,57:181-190.