A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority

We present an online ensemble approach, diversified dynamic weighted majority (DDWM) to classify new data instances which have varying conceptual distributions. Our approach maintains two sets of weighted ensembles that differentiate in their level of diversity. An expert in either of the ensembles is updated or removed as per its classification accuracy and a new expert is added based on the final global prediction of the algorithm and the global prediction of the ensemble for any data instance. Experimental evaluation using various artificial and real-world datasets proves that DDWM provides very high accuracy in classifying new data instances, irrespective of size of dataset, type of drift or presence of noise. We compare DDWM with the other learners in terms of new performance metrics such as kappa statistic, model cost, and the evaluation time and memory requirements. Our approach proved to be highly resource effective achieving very high accuracies even in a resource constrained environment.