| Literature DB >> 27066596 |
Ezilda Almeida1, Pedro Ferreira2, Tiago Vinhoza3, Inês Dutra2, Jingwei Li4, Yirong Wu4, Elizabeth Burnside4.
Abstract
Bayesian network structures are usually built using only the data and starting from an empty network or from a naïve Bayes structure. Very often, in some domains, like medicine, a prior structure knowledge is already known. This structure can be automatically or manually refined in search for better performance models. In this work, we take Bayesian networks built by specialists and show that minor perturbations to this original network can yield better classifiers with a very small computational cost, while maintaining most of the intended meaning of the original model.Entities:
Keywords: advice-based systems; bayesian networks; learning bayesian network structures
Year: 2014 PMID: 27066596 PMCID: PMC4826063 DOI: 10.1109/ICMLA.2014.64
Source DB: PubMed Journal: Proc Int Conf Mach Learn Appl