Book ChapterDOI
Rough membership and bayesian confirmation measures for parameterized rough sets
Salvatore Greco,Benedetto Matarazzo,Roman Słowiński +2 more
- pp 314-324
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TLDR
A generalization of the original idea of rough sets and variable precision rough sets is introduced, based on the concept of absolute and relative rough membership, aimed at modeling data relationships expressed in terms of frequency distribution.Abstract:
A generalization of the original idea of rough sets and variable precision rough sets is introduced. This generalization is based on the concept of absolute and relative rough membership. Similarly to variable precision rough set model, the generalization called parameterized rough set model, is aimed at modeling data relationships expressed in terms of frequency distribution rather than in terms of a full inclusion relation used in the classical rough set approach. However, differently from variable precision rough set model, one or more parameters modeling the degree to which the condition attribute values confirm the decision attribute value, are considered. The properties of this extended model are investigated and compared to the classical rough set model and the variable precision rough set model.read more
Citations
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Journal ArticleDOI
Probabilistic rough set approximations
TL;DR: Based on rough membership functions and rough inclusion functions, the Bayesian decision-theoretic analysis is adopted to provide a systematic method for determining the precision parameters by using more familiar notions of costs and risks.
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.
Book ChapterDOI
Decision-theoretic rough set models
TL;DR: It is shown that the decision-theoretic models need to consider additional issues in probabilistic rough set models.
Journal ArticleDOI
Probabilistic approach to rough sets
TL;DR: The article introduces the basic ideas and investigates the probabilistic version of rough set theory, which relies on both classification knowledge and Probabilistic knowledge in analysis of rules and attributes.
Journal ArticleDOI
Sequential covering rule induction algorithm for variable consistency rough set approaches
TL;DR: This work presents a general rule induction algorithm based on sequential covering, suitable for variable consistency rough set approaches, and shows how to improve rule induction efficiency due to application of consistency measures with desirable monotonicity properties.
References
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Book
The Logic of Scientific Discovery
TL;DR: The Open Society and Its Enemies as discussed by the authors is regarded as one of Popper's most enduring books and contains insights and arguments that demand to be read to this day, as well as many of the ideas in the book.
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.
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.
Book
Advances in the Dempster-Shafer theory of evidence
TL;DR: The Dempster-Shafer Theory of Evidence is applied as a guide for the management of uncertainty in knowledge-based systems.