Computer Science ›› 2018, Vol. 45 ›› Issue (7): 197-201.doi: 10.11896/j.issn.1002-137X.2018.07.034

• Artificial Intelligence • Previous Articles     Next Articles

Fast Attribute Reduction Algorithm Based on Importance of Voting Attribute

WANG Rong1,LIU Zun-ren2,JI Jun2   

  1. School of Data Science and Software Engineering,Qingdao University,Qingdao,Shandong 266071,China1;
    College of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China2
  • Received:2017-05-18 Online:2018-07-30 Published:2018-07-30

Abstract: As an extension of the classical Pawlak rough set,neighborhood rough sets can efficiently manipulate numerical data.However,because the concept of neighborhood granulation is introduced,computational complexity in the neighborhood real space is much larger than that in the classical discrete space.For the neighborhood rough set algorithm,it is very meaningful to find the attribute reduction of the data set efficiently and quickly.To this end,an improved definition of voting attribute importance was proposed for the shortcomings of the definition of attribute importance in existing algorithms,then a fast attribute reduction algorithm based onimportance of voting attribute was proposed.Compared with the existing algorithms,the experiment proves that the algorithm can get the attribute reduction more quickly under the premise of ensuring the classification accuracy.

Key words: Attribute reduction, Attribute significance, Neighborhood rough set, Vote

CLC Number: 

  • TP182
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