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Journal ArticleDOI

Multi knowledge based rough approximations and applications

H. M. Abu-Donia
- 01 Feb 2012 - 
- Vol. 26, pp 20-29
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TLDR
This paper presents new types of rough set approximations using multi knowledge base, that is, family of finite number of (reflexive, tolerance, dominance, equivalence) relations by two ways.
Abstract
Rough set theory is an important technique in knowledge discovery in databases. In covering based rough sets, many types of rough set models are established in recent years. This paper presents new types of rough set approximations using multi knowledge base, that is, family of finite number of (reflexive, tolerance, dominance, equivalence) relations by two ways. Properties and applications of these approximation operators are studied and many examples are given.

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Citations
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Journal ArticleDOI

Composite rough sets for dynamic data mining

TL;DR: An extended rough set model, called as composite rough sets, is presented, and a novel matrix-based method for fast updating approximations is proposed in dynamic composite information systems.
Journal ArticleDOI

Three-way group decision making based on multigranulation fuzzy decision-theoretic rough set over two universes

TL;DR: A new approach to multiple criteria group decision making problems, based on variable precision multigranulation fuzzy decision-theoretic rough set over two universes, and a cost-based method for sorting among all alternatives of group decision-making problems are established.
Journal ArticleDOI

Multigranulation fuzzy rough set over two universes and its application to decision making

TL;DR: The proposed models not only enrich the theory of multigranulation rough set but also make a tentative to provide a new perspective for multiple criteria group decision making with uncertainty.
Journal ArticleDOI

Approximations and uncertainty measures in incomplete information systems

TL;DR: Three types of definitions of lower and upper approximations and corresponding uncertainty measurement concepts including accuracy, roughness and approximation accuracy are investigated andoretical analysis indicates that two of the three types can be used to evaluate the uncertainty in incomplete information systems.
Journal ArticleDOI

Test cost sensitive multigranulation rough set: Model and minimal cost selection

TL;DR: It is shown that test cost sensitive multigranulation rough set is a generalization of optimistic, pessimistic and β-multigranulations rough sets, and a backtracking algorithm is proposed for granular structure selection with minimal test cost.
References
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Book

Rough Sets: Theoretical Aspects of Reasoning about Data

TL;DR: Theoretical Foundations.
Journal ArticleDOI

Rough sets

TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
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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

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.
Journal ArticleDOI

Relational interpretations of neighborhood operators and rough set approximation operators

TL;DR: This paper presents a framework for the formulation, interpretation, and comparison of neighborhood systems and rough set approximations using the more familiar notion of binary relations, and introduces a special class of neighborhood system, called 1-neighborhood systems.