Using positive region to reduce the computational complexity of discernibility matrix method

  • Authors:

  • Feng Honghai;Zhao Shuo;Liu Baoyan;He LiYun;Yang Bingru;Li Yueli

  • Affiliations:

  • Hebei Agricultural University, Baoding, China;Hebei Agricultural University, Baoding, China;China Academy of Traditional Chinese Medicine, Beijing, China;China Academy of Traditional Chinese Medicine, Beijing, China;University of Science and Technology Beijing, Beijing, China;Hebei Agricultural University, Baoding, China

  • Venue:
  • IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
  • Year:
  • 2006

Quantified Score

Hi-index 0.01

Visualization

Abstract

Rough set discernibility matrix method is a valid method to attribute reduction. However, it is a NP-hard problem. Up until now, though some methods have been proposed to improve this problem, the case is not improved well. We find that the idea of discernibility matrix can be used to not only the whole data but also partial data. So we present a new algorithm to reduce the computational complexity. Firstly, select a condition attribute C that holds the largest measure of γ(C, D) in which the decision attribute D depends on C. Secondly, with the examples in the non-positive region, build a discernibility matrix to create attribute reduction. Thirdly, combine the attributes generated in the above two steps into the attribute reduction set. Additionally, we give a proof of the rationality of our method. The larger the positive region is; the more the complexity is reduced. Four Experimental results indicate that the computational complexity is reduced by 67%, 83%, 41%, and 30% respectively and the reduced attribute sets are the same as the standard discernibility matrix method.