An Incremental Learning Approach for Updating Approximations in Rough Set Model Over Dual-Universes

Abstract

The rough set model over dual-universes (RSMDU) is a generalized model of the classical rough sets theory (RST) on the two universes. It is an effective way to use incremental updating approximations method in the dynamic environment to better support data mining-related tasks based on RST. In this paper, we propose an incremental learning approach for updating approximations in RSMDU when the objects of two universes vary with time. An illustration is employed to show the validation of the presented method. © Springer-Verlag Berlin Heidelberg 2014.