Computer Science ›› 2019, Vol. 46 ›› Issue (2): 236-241.doi: 10.11896/j.issn.1002-137X.2019.02.036

• Artificial Intelligence • Previous Articles     Next Articles

New Three-way Decisions Model Based on Granularity Importance Degree

XUE Zhan-ao, HAN Dan-jie, LV Min-jie, ZHAO Li-ping   

  1. College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China
    Engineering Lab of Henan Province for Intelligence Business & Internet of Things,Xinxiang,Henan 453007,China
  • Received:2017-12-13 Online:2019-02-25 Published:2019-02-25

Abstract: Granularity importance degree is an important content of multi-granularity rough set.Now,the construction methods of granularity importance degree only consider the direct influence of single granularity on decision-making,ignoring the combined effect of other granularity on decision-making.Combining the concept of multi-granularity approximated quality,this paper studied the construction method of granularity importance degree,proposed a new granularity importance degree calcuation method among multiple granularities,and gave a granular structure reduction algorithm based on this method.Meanwhile,in order to reduce the redundant decision information,through combining reduction set and three-way decisions,this paper constructed the three-way decisions model based on granularity importance degree,and gave the decision rules in detail.Finally,the results of an example show that the proposed granular degree reduction algorithm can obtain the data with larger discrimination,and narrows the range of delay domain,making the final decision more reasonable.

Key words: Multi-granularity rough set, Granularity importance degree, Granular structure reduction, Three-way decisions

CLC Number: 

  • TP181
[1]PAWLAK Z.Rough Set[J].International Journal of Computer and Information Sciences,1982,11(5):341-356.
[2]YAO Y Y.Probabilistic Rough Set Approximations[J].International Journal of Approximate Reasoning,2008,49(2):255-271.
[3]YAO Y Y,WONG S K M.A Decision Theoretic Framework for Approximating Concepts[J].International Journal of Man-machine Studies,1992,37(6):793-809.
[4]SLEZAK D,ZIARKO W.The Investigation of the Bayesian Rough Set Model[J].International Journal of Approximate Reasoning,2005,40(1-2):81-91.
[5]ZIARKO W.Variable Precision Rough Set Model[J].Journal of Computer and System Sciences,1993,46(1):39-59.
[6]YAO Y Y,LIN T Y.Generalization of Rough Sets Using Modal Logics[J].Intelligent Automation & Soft Computing,1996,2(2):103-119.
[7]LIN T Y.Granular Computing on Binary Relations II:Rough Set Representations and Belief Functions[J].Rough Sets in Knowledge Discovery,1998(1):122-140.
[8]QIAN Y H,LIANG J Y,YAO Y Y,et al.MGRS:A Multi-granu- lation Rough Set[J].Information Sciences,2010,180(6):949-970.
[9]QIAN Y H,LIANG J Y,DANG C Y.Incomplete Multi-granulation Rough Set[J].IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans,2010,40(2):420-431.
[10]SANG Y L,QIAN Y H.A Granular Space Reduction Approach to Pessimistic Multi-Granulation Rough Sets[J].Pattern Recognition and Artificial Intellgence,2012,25(3):361-366.(in Chinese)
[11]SANG Y L,QIAN Y H.Granular Structure Reduction Ap- proach to Multigranulation Decision-theoretic Rough Sets[J].Computer Science,2017,44(5):199-205.(in Chinese)
[12]DAI G Y,WANG Z M,YANG C,et al.A Multi-granularity Rough Set Algorithm for Attribute Reduction through Particles Particle Swarm Optimization[C]∥ Proceedings of International Computer Engineering Conference.New York:IEEE Press,2016:303-307.
[13]YANG X B,QI Y S,SONG X N,et al.Test Cost Sensitive Multigranulation Rough Set:Model and Minimal Cost Selection[J].Information Sciences,2013,250(11):184-199.
[14]JING Y G,LI T R,HFUJITA,et al.An Incremental Attribute Reduction Approach Based on Knowledge Granularity with a Multi-granulation View[J].Information Sciences,2017,411:23-38.
[15]YANG T,LI Z W,YANG X Q.A Granular Reduction Algo- rithm Based on Covering Rough Sets[J].Journal of Applied Mathematics,2012,2012:1-13.
[16]LI J H,REN Y,MEI C L,et al.A Comparative Study of Multigranulation Rough Sets and Concept Lattices via Rule Acquisition[J].Knowledge-Based Systems,2015,91:152-164.
[17]YAO Y Y.The Superiority of Three-way Decisions in Probabilistic Rough Set Models[J].Information Sciences,2011,181(6):1080-1096.
[18]YAO Y Y.Three-Way Decision:An Interpretation of Rules in Rough Set Theory[C]∥International Conference on Rough Sets and Knowledge Technology.Berlin:Springer,2009:642-649.
[19]LIANG D C,XU Z S,LIU D.Three-way Decisions with Intui- tionistic Fuzzy Decision-theoretic Rough Sets Based on Point Operators[J].Information Sciences,2017,375:183-201.
[20]QIAN J,DANG C Y,YUE X D,et al.Attribute Reduction for Sequential Three-way Decisions under Dynamic Granulation[J].International Journal of Approximate Reasoning,2017,85:196-216.
[21]XUE Z A,ZHU T L,XUE T Y,et al.Methodology of Attribute Weights Acquisition Based on Three-way Decision Theory[J].Computer Science,2015,42(8):265-268.(in Chinese)
[22]HU B Q.Three-way Decisions Space and Three-way Decisions [J].Information Sciences,2014,281:21-52.
[23]HU B Q,WONG H,YIU K F C.On Two Novel Types of Three-way Decisions in Three-way Decision Spaces[M].New York:Elsevier Science Inc.,2017.
[24]SHI J L,ZHANG Q Y,DU G Y.Multi-granularity Three-way Decision Model Based on Minimum Risks[J].Journal of Henan Normal University (Natural Science Edition),2017,45(2):101-107.(in Chinese)
[25]LI J H,HUANG C C,QI J J,et al.Three-way Cognitive Concept Learning via Multi-granularity[J].Information Sciences,2017,378(1):244-263.
[26]CHEN H,LI J H,MIN F,et al.Optimal Scale Selection in Dynamic Multi-scale Decision Tables Based on Sequential Three-way Decisions[J].Information Sciences,2017,415:213-232.
[27]XIAO J S,SUN L M.Improved Attribute Significance Degree Based on Rough Set[J].Computer Engineering and Applications,2017,53(3):174-176.(in Chinese)
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