A Boosting-like Online Learning Ensemble

Changes in the data distribution (concept drift) makes online learning a challenge that is progressively attracting more attention. This paper proposes Boosting-like Online Learning Ensemble (BOLE) based on heuristic modifications to Adaptable Diversity-based Online Boosting (ADOB), which is a modified version of Oza and Russell's Online Boosting. More precisely, we empirically investigate the effects of (a) weakening the requirements to allow the experts to vote and (b) changing the concept drift detection method internally used, aiming to improve the ensemble accuracy. BOLE was tested against the original and other modified versions of both boosting methods as well as three renowned ensembles using well-known artificial and real-world datasets and statistically surpassed the accuracies of both boosting methods as well as those of the three ensembles. The accuracy improved in most tested situations but this is more evident in the datasets with more concept drifts, where the accuracy gains were very high.