Journal ArticleDOI
Knowledge reduction in random information systems via Dempster-Shafer theory of evidence
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TLDR
It is proved that both of belief reduct and plausibility reduct are equivalent to classical reduct in (random) information systems.About:
This article is published in Information Sciences.The article was published on 2005-08-11. It has received 210 citations till now. The article focuses on the topics: Reduct & Dempster–Shafer theory.read more
Citations
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Journal ArticleDOI
Positive approximation: An accelerator for attribute reduction in rough set theory
TL;DR: A theoretic framework based on rough set theory, called positive approximation, is introduced, which can be used to accelerate a heuristic process of attribute reduction, and several representative heuristic attribute reduction algorithms inrough set theory have been enhanced.
Journal ArticleDOI
Attribute reduction in decision-theoretic rough set models
TL;DR: This paper addresses attribute reduction in decision-theoretic rough set models regarding different classification properties, such as decision-monotocity, confidence, coverage, generality and cost, and provides a new insight into the problem of attribute reduction.
Journal ArticleDOI
Granular Computing and Knowledge Reduction in Formal Contexts
Wei-Zhi Wu,Yee Leung,Ju-Sheng Mi +2 more
TL;DR: Granular structure of concept lattices with application in knowledge reduction in formal concept analysis is examined in this paper and knowledge hidden in such a context is unraveled in the form of compact implication rules.
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Discernibility matrix simplification for constructing attribute reducts
TL;DR: This paper proposes a reduct construction method based on discernibility matrix simplification, which works in a similar way to the classical Gaussian elimination method for solving a system of linear equations.
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Attribute reduction based on evidence theory in incomplete decision systems
TL;DR: This paper deals with attribute reduction in incomplete information systems and incomplete decision systems based on Dempster-Shafer theory of evidence and shows that in an incomplete information system an attribute set is a belief reduct if and only if it is a classical reduct and a plausibility consistent set must be a classical consistent set.
References
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Book
A mathematical theory of evidence
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
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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.
Book ChapterDOI
Upper and Lower Probabilities Induced by a Multivalued Mapping
TL;DR: A distinctive feature of the present approach is a rule for conditioning, or more generally, arule for combining sources of information, as discussed in Sects.
Journal ArticleDOI
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.