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

Abstract

The rough set model over dual universes RSMDU as a generalized model of classical rough set theory RST on the two universes has been well studied with the objective to establishment of model and discussion of its corresponding properties. Approximations of a concept in RSMDU, which may further be applied to knowledge discovery or related work, need to be updated effectively under a dynamic environment. Despite recent advances in using the incremental method to speed up updating approximations of RST, there has been little effort toward incorporating the incremental method into computing approximations under RSMDU. This paper proposes 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 proposed method. Extensive experimental results on various real and synthetic data sets verify the effectiveness of the proposed incremental updating method while comparing with the nonincremental method.