Abstract
Several researchers have studied the application of Machine Learning techniques to the task of user modeling. As most of them pointed out, this task requires learning algorithms that should work on-line, incorporate new information incrementality, and should exhibit the capacity to deal with concept-drift. In this paper we present Adaptive Bayes, an extension to the well-known naive-Bayes, one of the most common used learning algorithms for the task of user modeling. Adaptive Bayes is an incremental learning algorithm that could work on-line. We have evaluated Adaptive Bayes on both frameworks. Using a set of benchmark problems from the UCI repository [2], and using several evaluation statistics, all the adaptive systems show significant advantages in comparison against their non-adaptive versions.
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© 2002 Springer-Verlag Berlin Heidelberg
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Gama, J., Castillo, G. (2002). Adaptive Bayes. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_78
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DOI: https://doi.org/10.1007/3-540-36131-6_78
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