A New Combination of Diversity Techniques in Ensemble Classifiers for Handling Complex Concept Drift

Abstract

Recent advances in Computational Intelligent Systems have focused on addressing complex problems related to the dynamicity of the environments. Generally in dynamic environments, data are presented as streams that may evolve over time and this is known by concept drift. Handling concept drift through ensemble classifiers has received a great interest in last decades. The success of these ensemble methods relies on their diversity. Accordingly, various diversity techniques can be used like block-based data, weighting-data or filtering-data. Each of these diversity techniques is efficient to handle certain characteristics of drift. However, when the drift is complex, they fail to efficiently handle it. Complex drifts may present a mixture of several characteristics (speed, severity, influence zones in the feature space, etc.) which may vary over time. In this case, drift handling is more complicated and requires new detection and updating tools. For this purpose, a new ensemble approach, namely EnsembleEDIST2, is presented. It combines the three diversity techniques in order to take benefit from their advantages and outperform their limits. Additionally, it makes use of EDIST2, as drift detection mechanism, in order to monitor the ensemble’s performance and detect changes. EnsembleEDIST2 was tested through different scenarios of complex drift generated from synthetic and real datasets. This diversity combination allows EnsembleEDIST2 to outperform similar ensemble approaches in terms of accuracy rate, and present stable behaviors in handling different scenarios of complex drift.