A Variable Precision Rough Set Model based on Bayesian Learning

Abstract

Based on rough set theory, various data analysis methods have been studied. On the other hand, similar researches are also being conducted in the fields of machine learning and statistics. It is believed that by incorporating the findings of these fields into the rough set approach, it would be possible to develop new data analysis methods and integrate theories. In this study, for the variable precision rough set model, we propose rough membership values based on Bayesian learning. We propose a probability model that reflects the concept of rough set theory.