Journal ArticleDOI
MGRS: A multi-granulation rough set
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TLDR
It is shown that some of the properties of Pawlak's rough set theory are special instances of those of MGRS, and several important measures are presented, which are re-interpreted in terms of a classic measure based on sets, the Marczewski-Steinhaus metric and the inclusion degree measure.About:
This article is published in Information Sciences.The article was published on 2010-03-01. It has received 604 citations till now. The article focuses on the topics: Rough set & Dominance-based rough set approach.read more
Citations
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Three-way cognitive concept learning via multi-granularity
TL;DR: An axiomatic approach to describe three-way concepts by means of multi-granularity is put forward and a three- way cognitive computing system is designed to find composite three-Way cognitive concepts.
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Multigranulation decision-theoretic rough sets
TL;DR: The objective of this study is to develop a new multigranulation rough set model, called a multigsranulation decision-theoretic rough set, which can interprete the parameters from existing forms of probabilistic approaches to rough sets.
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Pythagorean fuzzy set: state of the art and future directions
TL;DR: An overview on Pythagorean fuzzy set is presented with aim of offering a clear perspective on the different concepts, tools and trends related to their extension, and two novel algorithms in decision making problems under Pythagorian fuzzy environment are provided.
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Sequential three-way decision and granulation for cost-sensitive face recognition
TL;DR: A sequential three-way decision method for cost-sensitive face recognition and a series of image granulation methods based on two-dimensional subspace projection methods, which simulate a sequential decision strategy from rough granule to precise granule.
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Concept learning via granular computing
TL;DR: Cognitive mechanism of forming concepts is analyzed based on the principles from philosophy and cognitive psychology, including how to model concept-forming cognitive operators, define cognitive concepts and establish cognitive concept structure to improve efficiency of concept learning.
References
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Journal ArticleDOI
Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic
TL;DR: M Modes of information granulation (IG) in which the granules are crisp (c-granular) play important roles in a wide variety of methods, approaches and techniques, but this does not reflect the fact that in almost all of human reasoning and concept formation thegranules are fuzzy (f- Granular).
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Rough fuzzy sets and fuzzy rough sets
Didier Dubois,Henri Prade +1 more
TL;DR: It is argued that both notions of a rough set and a fuzzy set aim to different purposes, and it is more natural to try to combine the two models of uncertainty (vagueness and coarseness) rather than to have them compete on the same problems.
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Rudiments of rough sets
Zdziasław Pawlak,Andrzej Skowron +1 more
TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.
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Variable precision rough set model
TL;DR: A generalized model of rough sets called variable precision model (VP-model), aimed at modelling classification problems involving uncertain or imprecise information, is presented and the main concepts are introduced formally and illustrated with simple examples.