Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 115-119.

• Intelligent Computing • Previous Articles     Next Articles

Distribution Attribute Reduction Based on Improved Discernibility Information Tree in Inconsistent System

LONG Bing-han1, XU Wei-hua2, ZHANG Xiao-yan2   

  1. School of Science,Chongqing University of Technology,Chongqing 400054,China1;
    School of Mathematics and Statistics,Southwest University,Chongqing 400715,China2
  • Online:2019-06-14 Published:2019-07-02

Abstract: Under the background of inconsistent systems,this paper studied how to effectively solve the problem of distributed attribute reduction.By using the judgment theorem of distributed coordination set,a new method of distributed attribute reduction under the background of inconsistent system was proposed.Inspired by difference matrix and discernibility information tree,in this method,an algorithm is constructed which uses the improved discernibility information tree to reduce the distribution attribute.The information tree realizes the compression and storage of non-empty ele-ments and redundant information in the discernibility matrix,and greatly simplifies the time complexity and the space complexity.

Key words: Distribution attribute reduction, Distribution coordination set, Improved discernibility information tree, Inconsistent system

CLC Number: 

  • TP181
[1]卢鹏,肖健梅,王锡淮,粗糙集属性约简的图论方法.计算机科学,2012,39(2):250-254.
[2]孙兴波,杨平先,干树川.基于属性重要度的启发式特征选取算法.自动化与仪器仪表,2005(5):13-14,17.
[3]徐伟华.序信息系统与粗糙集.北京:科学出版社,2013:28-32.
[4]李京政,杨习贝,窦慧莉,等.重要度集成的属性约简方法研究.智能系统学报,2018,5(9):1-8.
[5]MENG Z,SHI Z.On quick attribute reduction in decision-theoretic rough set models.Information Sciences,2016,330(C):226-244.
[6]陈昊,杨俊安,庄镇泉.变精度粗糙集的属性核和最小属性约简算法.计算机学报,2012,35(5):1011-1017.
[7]王国胤,姚一豫,于洪.粗糙集理论与应用研究综述.计算机学报,2009,32(7):1229-1246.
[8]张晓燕,徐伟华,张文修.序目标信息系统中分布约简的矩阵算法.理工大学学报,2010,24(3):56-61.
[9]蒋云良,杨章显,刘勇.不协调信息系统快速属性分布约简方法.自动化学报,2012,38(3):382-388.
[10]PANG J,ZHANG X,XU W.Attribute Reduction in Intuitionistic Fuzzy Concept Lattices.Abstract and Applied Analysis,2013(9):1-13.
[11]于海燕,乔晓东.一种完备的最小属性约简方法.计算机工程,2012,38(4):46-48.
[12]黄治国,王加阳,罗安.一种基于分布约简的规则获取方法.计算机应用研究,2007,24(6):42-44.
[13]XU W,LI W,LUO S.Knowledge reductions in generalized approximation space over two universes based on evidence theory.Journal of Intelligent & Fuzzy Systems,2015,28(6):2471-2480.
[14]汪凌.不协调决策信息系统的知识约简及决策规则优化研究.计算机应用研究,2019(7):1-6.
[15]蒋瑜.基于差别信息树Rough Set属性约简算法.控制与决策,2015,30(8):1531-1536.
[16]JU H,YANG X,YANG P,et al.A Moderate Attribute Reduction Approach in Decision-Theoretic Rough Set.Rough Sets,Fuzzy Sets,Data Mining,and Granular Computing.Sprin-ger International Publishing,2015.
[17]XU W,LI Y,LIAO X.Approaches to attribute reductions based on rough set and matrix computation in inconsistent ordered information systems.Knowledge-Based Systems,2012,27(3):78-91.
[18]YING,HE,DAN,et al.Discernibility Matrix-Based Attribute Reduction Algorithm of Decision Table.Advanced Materials Research,2012,457-458:1230-1234.
[19]尹继亮,张楠,童向荣,等.不协调区间值决策系统的最大分布约简.智能系统学报,2018,5(9):1-11.
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