期刊
FUZZY SETS AND SYSTEMS
卷 336, 期 -, 页码 1-26出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.fss.2017.06.010
关键词
Approximation operators; Covering; Fuzzy sets; Rough sets
资金
- Ghent University Special Research Fund
- Spanish Ministry of Science and Technology [TIN2014-57251-P]
- Andalusian Research Plans [P10-TIC-6858, P11-TIC-7765, P12-TIC-2958]
- Genil Program of CEI BioTic GRANADA [PYR-2014-8]
Fuzzy covering-based rough set models are hybrid models using both rough set and fuzzy set theory. The former is often used to deal with uncertain and incomplete information, while the latter is used to describe vague concepts. The study of fuzzy rough set models has provided very good tools for machine learning algorithms such as feature and instance selection. In this article, we discuss different types of dual fuzzy rough set models which all consider fuzzy coverings. In particular, we study two models using non-nested level-based representation of fuzziness. In addition to the study of the theoretical properties for each model, interrelationships between the different models are discussed, resulting in a Hasse diagram of fuzzy covering-based rough set models for a finite fuzzy covering, an IMTL-t-norm and its residual implicator. (c) 2017 Elsevier B.V. All rights reserved.
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