Title: A revised approach to solving the symbolic value partition problem from a viewpoint of roughness of partitions
Authors: Yasuo Kudo
Addresses: College of Information and Systems, Muroran Institute of Technology, 27-1 Mizumoto, Muroran 050-8585, Japan
Abstract: In this paper, we discuss a revision of a heuristic algorithm used to optimise symbolic value partitions that was proposed by Min et al. (2008) (RBSVP algorithm) from a viewpoint of the roughness of partitions. Min et al.'s approach for optimising symbolic value partitions was to minimise the number of attribute values used in a given decision table, and the RBSVP algorithm outputs a suboptimal symbolic value partition. However, in some cases, this approach might not contribute to the extraction of useful decision rules from the results of the optimised decision table. In this paper, instead of using the number of attribute values, we introduce the average coverage score that was proposed by Kudo and Murai (2010a) as a criterion of relative reducts based on the roughness of partitions, and we present an example of how the average coverage score is used to select relative reducts in the revised RBSVP algorithm. The results of our experiments indicate that the revised algorithm works well for extracting useful decision rules.
Keywords: rough sets; attribute reduction; symbolic value partition; SVP; evaluation criterion; heuristic algorithms; partition roughness; decision rules.
International Journal of Reasoning-based Intelligent Systems, 2012 Vol.4 No.3, pp.129 - 139
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 16 Nov 2012 *