Abstract
Concept drift due to hidden changes in context complicates learning in many domains including financial prediction, medical diagnosis, and network performance. Existing machine learning approaches to this problem use an incremental learning, on-line paradigm. Batch, off-line learners tend to be ineffective in domains with hidden changes in context as they assume that the training set is homogeneous.
We present an off-line method for identifying hidden context. This method uses an existing batch learner to identify likely context boundaries then performs a form of clustering called contextual clustering. The resulting data sets can then be used to produce context specific, locally stable concepts. The method is evaluated in a simple domain with hidden changes in context.
Michael Harries was supported by an Australian Postgraduate Award (Industrial).
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© 1998 Springer-Verlag Berlin Heidelberg
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Harries, M., Horn, K. (1998). Learning stable concepts in a changing world. In: Antoniou, G., Ghose, A.K., Truszczyński, M. (eds) Learning and Reasoning with Complex Representations. PRICAI 1996. Lecture Notes in Computer Science, vol 1359. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-64413-X_31
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DOI: https://doi.org/10.1007/3-540-64413-X_31
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