Investigation on Aggregated Weighted Ensemble Framework for Data Stream Classification

Abstract

Ensemble-based data stream classification process is the most active research work in strengthening the efficacy of ensemble-based data stream classification process. This research is carried out in two different dimensions. First is focused on devising novel ensemble-based data stream classification algorithms to enrich data stream classification task. The second dimension is focused on formulating novel frameworks which propose the novel strategies to aggregate the results of the off-the-shelf classification algorithms. The proposed research work expounds a novel aggregated weighted ensemble framework that aggregates the results of off-the-shelf classification algorithms for data stream classification. The architecture and working principles of the proposed framework, and its role in confronting concept drifts in the data stream classification task are experimentally investigated. Theoretical justifications and empirical evaluation are made on the proposed framework, and accentuate the competency of the proposed framework in terms of accuracy.