序信息系统中的双量化决策粗糙集模型
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摘要
决策粗糙集和程度粗糙集作为两类广义的粗糙集模型,分别从相对信息量化和绝对信息量化的观点对经典的粗糙集模型进行了扩张。本文在序信息系统中将这两类广义粗糙集模型进行融合,即将决策粗糙集和程度粗糙集的近似算子重新组合,以此构造了两种不同类型的双量化粗糙集模型。然后,对模型的一些基本性质进行了讨论,新建立的粗糙集模型包含了相对量化信息和绝对量化信息。最后,通过对实际案例分析展现了双量化决策规则的获取方法,结果表明两类模型均可以有效的获取决策规则,本文为序信息系统中的决策分析提供了新的选择。
The decision-theoretic rough set and graded rough set are two kinds of generalized rough models. They expand the classical rough set model from the point of view of relative information and absolute information quantization, respectively. This study fuses these two generalized rough set models in an ordered information system. It means that recombining the approximate operators of decision-theoretic rough set and graded rough set to construct two different types of double quantization rough set models. Then, some essential properties of these new established models are addressed. The novel models incorporate the relative information and absolute quantitative informa-tion. Finally, the approach of achieving double quantization decision rules is represented through an actual case analysis. The results indicate that two kinds of models can effectively obtain decision rules. This investigation provides a new choice for decision analysis in an ordered information system.
The decision-theoretic rough set and graded rough set are two kinds of generalized rough models. They expand the classical rough set model from the point of view of relative information and absolute information quantization, respectively. This study fuses these two generalized rough set models in an ordered information system. It means that recombining the approximate operators of decision-theoretic rough set and graded rough set to construct two different types of double quantization rough set models. Then, some essential properties of these new established models are addressed. The novel models incorporate the relative information and absolute quantitative informa-tion. Finally, the approach of achieving double quantization decision rules is represented through an actual case analysis. The results indicate that two kinds of models can effectively obtain decision rules. This investigation provides a new choice for decision analysis in an ordered information system.
引文
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[17]Dembczyński K,Pindur R,Susmaga R.Dominance-based rough set classifier without induction of decision rules[J].Electronic Notes in Theoretical Computer Science,2003,82(4):84~95.
[18]徐伟华.序信息系统与粗糙集[M].北京:科学出版社,2013:18~29.
[19]张文修等.粗糙集理论与方法[M].北京:科学出版社,2001:118~121.
[2]梁曼等.基于粗糙集的航路飞行冲突智能解脱系统案例检索方法[J].科学技术与工程,2015,15(3):289~294.
[3]Shen Q,Chouchoulas A.rough-fuzzy approach for generating classification rules[J].Pattern Recognition,2002,35(11):2425~2438.
[4]Zeng A,etal.Knowledge acquisition based on rough set theory and principal component analysis[J].IEEE Intelligent Systems,2006,21(2):78~85.
[5]张艳敏,庞帮艳.基于粗糙集的传感网络节点故障诊断方法研究[J].科学技术与工程,2016,16(27):231~235.
[6]Yao Y.Probabilistic rough set approximations[J].International Journal of Approximate Reasoning,2008,49(2):255~271.
[7]Ziarko W.Variable precision rough set model[J].Journal of Computer System Sciences,1993,46(1):39~59.
[8]Slezak D,Ziarko W.The investigation of the Bayesian rough set model[J].International Journal of Approximate Reasoning,2005,39(1):81~91.
[9]Azam N,Yao T.Interpretation of equilibria in game-theoretic rough sets[J].Information Sciences,2015,295(3~4):586~599.
[10]Greco S,et al.Parameterized rough set model using rough membership and Bayesian confirmation measures[J].International Journal of Approximate Reasoning,2008,49(2):285~300.
[11]Yao Y,Lin T Y.Generalization of rough sets using modal logics[J].Intelligent Automation&Soft Computing,1996,2(2):103~119.
[12]Yao Y,Wong S K M.A decision theoretic framework for approximating concepts[J].International Journal of Man-Machine Studies,992,37(6):793~809.
[13]桑妍丽,钱宇华.多粒度决策粗糙集中的粒度约简方法[J].计算机科学,2017,44(5):199~205.
[14]Li W,Xu W.Double-quantitative decision-theoretic rough set[J].Information Sciences,2015,316:54~67.
[15]Zhang X,Miao D.Quantitative information architecture,granular computing and rough set models in the doublequantitative approximation space of precision and grade[J].Information Sciences,2014,268(2):147~168.
[16]Zhang X,Miao D.Double-quantitative fusion of accuracy and importance:Systematic measure mining,benign integration construction,hierarchical attribute reduction[J].Knowledge-Based Systems,2016.
[17]Dembczyński K,Pindur R,Susmaga R.Dominance-based rough set classifier without induction of decision rules[J].Electronic Notes in Theoretical Computer Science,2003,82(4):84~95.
[18]徐伟华.序信息系统与粗糙集[M].北京:科学出版社,2013:18~29.
[19]张文修等.粗糙集理论与方法[M].北京:科学出版社,2001:118~121.