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Soft Computing

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26.07.2018 | Foundations

Maximal similarity granular rough sets for mixed and incomplete information systems

verfasst von: Yenny Villuendas-Rey

Erschienen in: Soft Computing | Ausgabe 13/2019

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Abstract

Mixed and incomplete data is very common in several applications nowadays. Unfortunately, Rough Sets lack effective tools for handling both mixed as well as incomplete information systems. This paper introduces a novel approach for dealing with such information systems: The Generic Extended Rough Sets and the maximal similarity granular rough sets (MSGRS) as a particular case. MSGRS have single and multiple granulations, as well as optimistic and pessimistic definitions for both scenarios. The theoretical analysis carried out, as well as the proposed notation, shows that MSGRS are a generalization of some existing proposals for dealing with incomplete information systems and mixed information systems. The obtained results enrich rough Set Theory and are useful for addressing mixed as well as incomplete information systems, with single and multiple granulations.

Vorheriger Artikel A latent space-based estimation of distribution algorithm for large-scale global optimization

Nächster Artikel Variations of the free implicative semilattice extension of a Hilbert algebra

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Titel: Maximal similarity granular rough sets for mixed and incomplete information systems
verfasst von: Yenny Villuendas-Rey
Publikationsdatum: 26.07.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 13/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3408-2

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