Rough set based fuzzy modeling by occupancy degree and optimal partition of projection

Abstract

The rough set theory suggested by Pawlak has a property that it can represent the degree of consistency between condition and decision attributes of data pairs which don't have linguistic information. In this paper, by using this ability of rough set theory, we define a measure called occupancy degree which can represent a consistency degree of premise and consequent variables in fuzzy rules describing experimental data pairs. We also propose a method by which we partition the projected data on input space and find an optimal fuzzy rule table and membership functions of input and output variables from data without preliminary linguistic information.