The investigation of the Bayesian rough set model

  • Authors:

  • Dominik lezak;Wojciech Ziarko

  • Affiliations:

  • Department of Computer Science University of Regina, Regina, SK, Canada S4S 0A2;Department of Computer Science University of Regina, Regina, SK, Canada S4S 0A2

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

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.