MDL-based attribute models in naive Bayes classification
In: Second Workshop on Information Theoretic Methods in Science and Engineering, 17-19 August, 2009, Tampere, Finland.
When classifying objects with Naive Bayes classiﬁers, we are faced with the problem of how to handle continuous attributes. Common solutions to this problem are discretizing, or assuming the data to be normally distributed. In this paper we take a different approach and instead model
the class-speciﬁc attribute distributions of Na¨ıve Bayes classiﬁers with MDL-optimal histogram density functions. We present experimental results, comparing MDL-optimal histograms to Gaussian distributions and histograms learned
with other methods.