Computer Science ›› 2016, Vol. 43 ›› Issue (6): 218-222.doi: 10.11896/j.issn.1002-137X.2016.06.044

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New Heuristic Algorithm for Attribute Reduction in Decision-theoretic Rough Set

CHANG Hong-yan and MENG Zu-qiang   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Attribute reduction is one of the most important research contents in rough set theory.Scholars have proposed various definitions for attribute reduction in decision-theoretic rough set,including the definition of keep the positive decisions of all objects unchanged.Directing at the positive decision definition,in order to efficiently obtain the reduction set,designed a heuristic function is designed,that is important degree of decision-making.This heuristic function defines the decision important degree of every attribute according to the size of positive decision objects set.The bigger the size of positive decision objects set,the greater the improtance,thus constructs heuristic attribute reduction algorithm based on the decision important degree.The advantage of this algorithm is that it determines the search direction according to the sorting of attribute decision important degree,avoids the calculation of attribute combination,and can reduce the amount of calculation and find out a smaller reduction set.The experimental results show that the algorithm is effective and can obtain a good reduction effect.

Key words: Decision-theoretic rough set,Attribute reduction,Heuristic function

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