Computer Science ›› 2014, Vol. 41 ›› Issue (12): 148-150.doi: 10.11896/j.issn.1002-137X.2014.12.031

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Attribute Reduction with Principle of Minimum Correlation and Maximum Dependency

ZHAI Jun-hai,WAN Li-yan and WANG Xi-zhao   

  • Online:2018-11-14 Published:2018-11-14

Abstract: In the classical rough set,the reduction algorithm based on significance for decision table only considers the dependency of decision attribute and condition attribute,and does not consider the correlation between the condition attributes in reduct.The reduct calculated with this kind of algorithm may include redundant attributes.In order to deal with this problem,an improved algorithm was proposed in this paper,which calculates the reduct with the principle of minimum correlation and maximum dependency.Compared with the reduction algorithm based on significance for decision table,less attributes are remained in the reducts calculated with the proposed algorithm,and the redundancy of the reduct is smaller.The experimental results show that the proposed algorithm outperforms the reduction algorithm based on significance for decision table.

Key words: Rough sets,Decision table,Attribute reduct,Minimum correlation,Maximum dependency

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