Abstract
The prior distribution of an attribute in a naive Bayesian classifier is typically assumed to be a Dirichlet distribution, and this is called the Dirichlet assumption. The variables in a Dirichlet random vector can never be positively correlated and must have the same confidence level as measured by normalized variance. Both the generalized Dirichlet and the Liouville distributions include the Dirichlet distribution as a special case. These two multivariate distributions, also defined on the unit simplex, are employed to investigate the impact of the Dirichlet assumption in naive Bayesian classifiers. We propose methods to construct appropriate generalized Dirichlet and Liouville priors for naive Bayesian classifiers. Our experimental results on 18 data sets reveal that the generalized Dirichlet distribution has the best performance among the three distribution families. Not only is the Dirichlet assumption inappropriate, but also forcing the variables in a prior to be all positively correlated can deteriorate the performance of the naive Bayesian classifier.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.