Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems

Abstract

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