The investigation of the Bayesian rough set model
Dominik lezak,Wojciech Ziarko +1 more
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TLDR
A non-parametric modification of the VPRS model called the Bayesian Rough Set (BRS) model is presented, where the set approximations are defined by using the prior probability as a reference.About:
This article is published in International Journal of Approximate Reasoning.The article was published on 2005-07-01 and is currently open access. It has received 382 citations till now. The article focuses on the topics: Dominance-based rough set approach & Rough set.read more
Citations
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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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Rough sets: Some extensions
Zdzisław Pawlak,Andrzej Skowron +1 more
TL;DR: Some extensions of the rough set approach are presented and a challenge for the roughSet based research is outlined and it is outlined that the current rough set based research paradigms are unsustainable.
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MGRS: A multi-granulation rough set
TL;DR: 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.
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The superiority of three-way decisions in probabilistic rough set models
TL;DR: It is shown that, under certain conditions when considering the costs of different types of miss-classifications, probabilistic three-way decisions are superior to the other two.
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.
References
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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 ChapterDOI
The Discernibility Matrices and Functions in Information Systems
Andrzej Skowron,Cecylia Rauszer +1 more
TL;DR: In this article, the authors introduce two notions related to any information system, namely the discernibility matrix and discernibility function, and obtain several algorithms for solving problems related among other things to the rough definability, reducts, core and dependencies generation.
Book
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
TL;DR: The use of 'Rough Sets' Methods to draw Premonitory Factors for Earthquakes by emphasising Gas Geochemistry: The Case of a Low Seismic Activity Context in Belgium J.T. Polkowski is used.
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.