Adaptive ensemble based learning in non-stationary environments with variable concept drift

Authors:
Teo Susnjak;Andre L. C. Barczak;Ken A. Hawick
Affiliations:
Massey University, Albany, New Zealand;Massey University, Albany, New Zealand;Massey University, Albany, New Zealand
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Year:
2010

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Abstract

The aim of this paper is to present an alternative ensemblebased 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 realtime 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 timeevolving environments with unknown rates of change and is capable of handling large volume data-streams in real-time.