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Research of interval reduction in variable precision rough set

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Research of interval reduction in variable precision rough set

Content Provider	IET Digital Library
Author	Chen, Shenghai Jiang, Fengying Mi, Xianwu
Abstract	The essence of reduction is to find a minimal attribute subset having the same classification ability without inducing new inconsistency with a given decision information system. In variable precision rough set, interval reduction based on β-classification quality leads to several kinds of reduction anomalies. In this study, the authors define interval reduction based on β-lower approximation distribution to avoid all kinds of reduction anomalies. The merger of condition classes is discussed and a method is presented to get interval reduction based on the ordered discernibility matrix of condition classes. The property of interval core attribute which is the most important attribute on the given β -interval is also discussed and a method is given to calculate core attribute on β-lower approximation distribution hierarchy. In addition, the core attribute set is usually used as the original subset in the heuristic algorithm for attribute reduction.
Starting Page	561
Ending Page	565
Page Count	5
Volume Number	2020
e-ISSN	20513305
Issue Number	Issue 13, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2020/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1194
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-01-13
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Approximation Theory Attribute Reduction Combinatorial Mathematics Condition Classes Data Handling Technique Data Mining Decision Information System Interpolation And Function Approximation Interval Reduction Knowledge Engineering Technique Linear Algebra Matrix Algebra Minimal Attribute Subset Numerical Analysis Pattern Classification Rough Set Theory Variable Precision Rough Set Β-classification Quality Β-lower Approximation Distribution Hierarchy
Content Type	Text
Resource Type	Article

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