Minimal attribute reduction with rough set based on compactness discernibility information tree |
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Authors: | Yu Jiang Yang Yu |
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Affiliation: | 1.College of Software Engineering,Chengdu University of Information Technology,Chengdu,China;2.College of Electric Information Engineering,Jiangsu University of Technology,Changzhou,China |
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Abstract: | Minimal attribute reduction plays an important role in rough set. Heuristic algorithms are proposed in literature reviews to get a minimal reduction and yet an unresolved issue is that many redundancy non-empty elements involving duplicates and supersets exist in discernibility matrix. To be able to eliminate the related redundancy and pointless elements, in this paper, we propose a compactness discernibility information tree (CDI-tree). The CDI-tree has the ability to map non-empty elements into one path and allow numerous non-empty elements share the same prefix, which is recognized as a compact structure to store non-empty elements in discernibility matrix. A complete algorithm is presented to address Pawlak reduction based on CDI-tree. The experiment results reveal that the proposed algorithm is more efficient than the benchmark algorithms to find out a minimal attribute reduction. |
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