Abstract
Naive Bayesian classifiers are used in a large range of application domains. These models generally show good performance despite their strong underlying assumptions. In this paper, we demonstrate however, by means of an example probability distribution, that a data set of instances can give rise to a classifier with counterintuitive behaviour. We will argue that such behaviour can be attributed to the learning algorithm having constructed incorrect directions of monotonicity for some of the feature variables involved. We will further show that conditions can be derived for the learning algorithm to retrieve the correct directions.
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Pieters, B.F.I., van der Gaag, L.C., Feelders, A. (2011). When Learning Naive Bayesian Classifiers Preserves Monotonicity. In: Liu, W. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2011. Lecture Notes in Computer Science(), vol 6717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22152-1_36
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DOI: https://doi.org/10.1007/978-3-642-22152-1_36
Publisher Name: Springer, Berlin, Heidelberg
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