NDLI: Minimal Element Selection in the Discernibility Matrix for Attribute Reduction

Content Provider	IET Digital Library
Author	Jiang, Yu
Abstract	Discernibility matrix is a beautiful theoretical result to get reducts in the rough set, but the existing algorithms based on discernibility matrix share the same problem of heavy computing load and large store space, since there are numerous redundancy elements in discernibility matrix and these algorithms employ all elements to find reducts. We introduce a new method to compute attribute significance. A novel approach is proposed, called minimal element selection tree, which utilizes many strategies to eliminate redundancy elements in discernibility matrix. This paper presents two methods to find out a minimal reduct for a given decision table based this tree structure. The experimental results with UCI data show that the proposed approaches are effective and efficient than the benchmark methods.
Starting Page	6
Ending Page	12
Page Count	7
ISSN	10224653
Volume Number	28
e-ISSN	20755597
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/28/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2018.06.018
Journal	Chinese Journal of Electronics
Publisher Date	2019-01-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Algebra Attribute Reduction Combinatorial Mathematics Data Handling Technique Data Reduction Decision Table Decision Tables Discernibility Matrix Share Large Store Space Matrix Algebra Minimal Element Selection Tree Redundancy Element Rough Set Rough Set Theory Tree Structure UCI Data
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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