Hybridization of rough sets and statistical learning theory

Abstract

In this paper we propose the hybridization of the rough set concepts and statistical learning theory. We introduce new estimators for rule accuracy and coverage, which base on the assumptions of the statistical learning theory. These estimators allow us to select rules describing statistically significant dependencies in data. Then we construct classifier which uses these estimators for rule induction. In order to make our solution applicable for information systems with missing values and multiple valued attributes, we propose axiomatic representation of information systems and we redefine the indiscernibility relation as a relation on objects characterized by axioms. Finally, we test our classifier on benchmark datasets. © 2011 Springer-Verlag Berlin Heidelberg.