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

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.