Abstract
Virtual concept drift is a phenomenon frequently arising in applications of machine learning theory. Most commonly, a discard-retrain strategy is the only option for dealing with newly generated data coming from previously unknown areas of the input space. This paper proposes a method of constructing classifier ensembles based on a measure of observations’ density and homogeneity of their corresponding labels. The strategy allows to incrementally add new data points into the model without the necessity of a full retraining procedure.
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Bartocha, K., Podolak, I.T. (2011). Classifier Ensembles for Virtual Concept Drift – The DEnBoost Algorithm. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_20
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DOI: https://doi.org/10.1007/978-3-642-21222-2_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21221-5
Online ISBN: 978-3-642-21222-2
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