Abstract
The aim of this paper is to present an alternative ensemble-based drift learning method that is applicable to cascaded ensemble classifiers. It is a hybrid of detect-and-retrain and constant-update approaches, thus being equally responsive to both gradual and abrupt concept drifts. It is designed to address the issues of concept forgetting, experienced when altering weights of individual ensembles, as well as real-time adaptability limitations of classifiers that are not always possible with ensemble structure-modifying approaches. The algorithm achieves an effective trade-off between accuracy and speed of adaptations in time-evolving environments with unknown rates of change and is capable of handling large volume data-streams in real-time.
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Susnjak, T., Barczak, A.L.C., Hawick, K.A. (2010). Adaptive Ensemble Based Learning in Non-stationary Environments with Variable Concept Drift. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_54
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DOI: https://doi.org/10.1007/978-3-642-17537-4_54
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