On quick attribute reduction in decision-theoretic rough set models-Reference-Cited by-同舟云学术

On quick attribute reduction in decision-theoretic rough set models

Published:2016-02 Issue: Volume:330 Page:226-244
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Meng Zuqiang^ORCID,Shi Zhongzhi

Funder

National Natural Science Foundation of China

National Basic Research Programme of China

Natural Science Foundation of Guangxi Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference59 articles.

1. A more efficient attribute self-adaptive co-evolutionary reduction algorithm by combining quantum elitist frogs and cloud model operators;Ding;Inf. Sci.,2015

2. Rule learning for classification based on neighborhood covering reduction;Du;Inf. Sci.,2011

3. Rough membership and Bayesian confirmation measures for parameterized rough sets;Greco;Lect. Notes Artif. Intell.,2005

4. Generalized probabilistic approximations of incomplete data;Grzymala-Busse;Int. J. Approx. Reason.,2014

5. Minimum cost attribute reduction in decision-theoretic rough set models;Jia;Inf. Sci.,2013

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