Generalized discernibility function based attribute reduction in set-valued decision systems

Abstract

Rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most of attribute reduction methods are performed on single-valued decision system decision table. In this paper, we propose methods for attribute reduction in static set-valued decision systems and dynamic set-valued decision systems with dynamically-increasing and decreasing conditional attributes. The methods use generalized discernibility matrix and function in tolerance-based rough sets.