Using unsupervised incremental learning to cope with gradual concept drift

Abstract

Current computational approach to incremental learning requires a constant stream of labelled data to cope with gradual environmental changes known as concept drift. This paper examines a case where labelled data are unavailable. Inspired by the performance of the human visual system, capable of adjusting its concepts using unlabelled stimuli, we introduce a variant to an unsupervised competitive learning algorithm known as the Leader Follower (LF). This variant can adjust pre-learned concepts to environmental changes using unlabelled data samples. We motivate the needed change in the existing LF algorithm and compare between two variants to enable the accumulation of environmental changes when facing unbalanced sample ratio.