PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

MDL-based attribute models in naive Bayes classification
Petri Myllymäki
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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:5906
Deposited By:Petri Myllymäki
Deposited On:08 March 2010