Journal of Guangxi Normal University(Natural Science Edition) ›› 2011, Vol. 29 ›› Issue (3): 94-100.

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Rules Extraction Method Based on Equivalence Describe Matrix

YAN Lin, LIANG Ji-ye, WANG Jun-hong   

  1. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University,Taiyuan Shanxi 030006,China
  • Received:2011-05-05 Online:2011-08-20 Published:2018-12-03

Abstract: Rough set method is an effective method of classification,but with high-dimensional data,it is difficult to rely on reduction to extract the rules which have high generalization capability,because the reductionof rough set itself fails to notice the effect of the object on the informationsystem to a certain degree.This paper describes the differences between different objects and extracts the classified information of every object.Then a new rules extraction method is designed based on rough set.And the algorithm is provedto have better generalization capability than the traditional one.

Key words: rough set, attribute reduction, decision datasets, equivalence class, equivalence describe matrix

CLC Number: 

  • TP18
[1] PAWLAK Z.Rough set[J].International Journal of Computer and InformationScience,1982,11(5):341-356.
[2] PAWLAK Z.Rough sets theoretical aspects of reasoning about data[M].Boston,Mass:Kluwer Academic Publisher,1991.
[3] 张文修,吴伟志,梁吉业.粗糙集理论与方法[M].北京:科学出版社,2001.
[4] PAWLAK Z,SKOWRON A.Rudiments of rough sets[J].Information Science,2007,117(1):3-27.
[5] QIAN Y H,LIANG J Y.Positive approximation:an accelerator for attribute reduction in rough set theory[J].Artificial Intelligence,2010,174(9/10):597-618.
[6] THANGAVEL K,PETHALAKSHMI A.Dimensionality reduction based on rough set theory:a review[J].Applied Soft Computing,2009,9(1):1-12.
[7] YAO Y Y,ZHAO Y.Discernibility matrix simplification for constructing attribute reducts[J].Information Sciences,2009,179(7):867-882.
[8] LI J,WANG X,FAN X W.Improved binary discernibility matrix attribute reduction algorithm in customer relationship management[J].Procedia Engineering,2010,7:473-476.
[9] HU Xiao-hua,CERCONE N.Learning in relational databases:a rough set approach[J].Computational Intelligence,1995,11(2):323-337.
[10] QIAN Y H,LIANG J Y,LI D Y.Measures for evaluating the decision performanceof a decision table in rough set theory[J].Information Sciences,2008,178(1/2):181-202.
[11] LIANG J Y,SHI Z,LI D Y.The information entropy,rough entropy andknowledge granulation in rough set theory[J].International Journal of Uncertainty Fuzziness and Knowledge-Based Systems,2004,12(1):37-46.
[12] LIN T Y,CERCONE N.Rough sets and data mining:analysis of imprecise data[M].Boston,Mass:Kluwer Academic Publisher,1997.
[13] 张文修,姚一豫,梁怡.粗糙集与概念格[M].西安:西安交通大学出版社,2006.
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