Abstract
The fundamental assumption that training and operational data come from the same probability distribution, which is the basis of most learning algorithms, is often not satisfied in practice. Several algorithms have been proposed to cope with classification problems where the class priors may change after training, but they can show a poor performance when the class conditional data densities also change. In this paper, we propose a re-estimation algorithm that makes use of unlabeled operational data to adapt the classifier behavior to changing scenarios. We assume that (a) the classes may be decomposed in several (unknown) subclasses, and (b) the prior subclass probabilities may change after training. Experimental results with practical applications show an improvement over an adaptive method based on class priors, while preserving a similar performance when there are no subclass changes.
Supported by the Spanish MEC projects DPI2006-02550 and TEC2008-01348/TEC.
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© 2009 Springer-Verlag Berlin Heidelberg
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Alaiz-Rodríguez, R., Guerrero-Curieses, A., Cid-Sueiro, J. (2009). Improving Classification under Changes in Class and Within-Class Distributions. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_16
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DOI: https://doi.org/10.1007/978-3-642-02478-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02477-1
Online ISBN: 978-3-642-02478-8
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