可区分度与全粒度属性约简

doi:10.16451/j.cnki.issn1003-6059.201908003

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Abstract

It is difficult to calculate attribute reduct due to high time and space complexity of entire-granulation rough sets. To solve this problem, distinguishability in information systems is defined by equivalent class, and its properties are studied. It is proved that the attribute reduct based on distinguishability is equivalent to absolute reduct. The positive region distinguishability in decision systems is defined and its properties are discussed. It is also proved that positive region distinguishability reduct is a superset of entire-granulation Pawlak reduct, but in most cases it is equal to entire-granulation Pawlak reduct and it can be regarded as an approximation of entire-granulation Pawlak reduct. Theoretical analysis and experiments show that compared with other attribute reduction algorithms, positive region distinguishability reduct has great advantages in computational complexity and classification accuracy.

Key words： Entire-Granulation Rough Sets Distinguishability Attribute Reduction Positive Region Distinguishability Entire-Granulation Pawlak Reduct

Received: 04 March 2019

ZTFLH:

TP 18

Fund:

Supported by Zhejiang Normal University: A Top Discipline of Cyberspace Security in Zhejiang Province

Corresponding Authors: DENG Dayong(Corresponding author), Ph.D., associate professor. His research interests include rough sets, granular computing and data mining.

About author:: YAO Kun, master student. His research interests include rough sets.WU Yue, master student. His research interests include rough sets.

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Cite this article:

YAO Kun,DENG Dayong,WU Yue. Distinguishability and Attribute Reduction for Entire-Granulation Rough Sets[J]. , 2019, 32(8): 699-708.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201908003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I8/699

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