Online approach to handle concept drifting data streams using diversity

Concept drift is the trend observed in almost all real time applications. Many online and offline algorithms were developed in the past to analyze this drift and train our algorithms. Different levels of diversity are required before and after a drift to get the best generalization accuracy. In our paper, we present a new online approach Extended Dynamic Weighted Majority with diversity (EDWM) to handle various types of drifts from slow gradual to abrupt drifts. Our approach is based on the Weighted Majority(WM) vote of the ensembles containing different diversity levels. Experiments on the various artificial and real datasets proved that our proposed ensemble approach learns drifting concepts better than the existing online approaches in a resource constrained environment.