Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems

  • Authors:

  • Junbo Zhang;Tianrui Li;Da Ruan;Dun Liu

  • Affiliations:

  • School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;Belgian Nuclear Research Centre (SCK•CEN), Boeretang 200, 2400 Mol, Belgium and Department of Applied Mathematics & Computer Science, Ghent University, 9000 Gent, Belgium;School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, China

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2012

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Abstract

Set-valued information systems are generalized models of single-valued information systems. The attribute set in the set-valued information system may evolve over time when new information arrives. Approximations of a concept by rough set theory need updating for knowledge discovery or other related tasks. Based on a matrix representation of rough set approximations, a basic vector H(X) is induced from the relation matrix. Four cut matrices of H(X), denoted by H^[^@m^,^@n^](X), H^(^@m^,^@n^](X), H^[^@m^,^@n^)(X) and H^(^@m^,^@n^)(X), are derived for the approximations, positive, boundary and negative regions intuitively. The variation of the relation matrix is discussed while the system varies over time. The incremental approaches for updating the relation matrix are proposed to update rough set approximations. The algorithms corresponding to the incremental approaches are presented. Extensive experiments on different data sets from UCI and user-defined data sets show that the proposed incremental approaches effectively reduce the computational time in comparison with the non-incremental approach.