A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets

  • Authors:

  • Chen Degang;Wang Changzhong;Hu Qinghua

  • Affiliations:

  • Department of Mathematics and Physics, North China Electric Power University, 102206 Beijing, PR China;Department of Mathematics, Harbin Institute of Technology, 150001 Harbin, PR China;Power Engineering, Harbin Institute of Technology, 150001 Harbin, PR China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2007

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Abstract

Traditional rough set theory is mainly used to extract rules from and reduce attributes in databases in which attributes are characterized by partitions, while the covering rough set theory, a generalization of traditional rough set theory, does the same yet characterizes attributes by covers. In this paper, we propose a way to reduce the attributes of covering decision systems, which are databases characterized by covers. First, we define consistent and inconsistent covering decision systems and their attribute reductions. Then, we state the sufficient and the necessary conditions for reduction. Finally, we use a discernibility matrix to design algorithms that compute all the reducts of consistent and inconsistent covering decision systems. Numerical tests on four public data sets show that the proposed attribute reductions of covering decision systems accomplish better classification performance than those of traditional rough sets.