The investigation of the Bayesian rough set model
International Journal of Approximate ReasoningPublished 7 January 2005
Dominik Ślȩzak, Wojciech Ziarko
Citations397
SJR quartileQ2
SJR score0.73
SNIP1.18
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TL;DR
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.
Abstract
The original Rough Set model is concerned primarily with algebraic properties of approximately defined sets. The Variable Precision Rough Set (VPRS) model extends the basic rough set theory to incorporate probabilistic information. The article presents a non-parametric modification of the VPRS model called the Bayesian Rough Set (BRS) model, where the set approximations are defined by using the prior probability as a reference. Mathematical properties of BRS are investigated. It is shown that the quality of BRS models can be evaluated using probabilistic gain function, which is suitable for identification and elimination of redundant attributes.
Keywords
Computer ScienceDecision Sciences
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