When Learning Naive Bayesian Classifiers Preserves Monotonicity.
ECSQARU'11: Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty(2011)
摘要
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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关键词
counterintuitive behaviour,Naive Bayesian classifier,application domain,correct direction,example probability distribution,feature variable,good performance,incorrect direction,large range,strong underlying assumption,naive bayesian classifier
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