TítuloGaussian Join Tree classifiers with applications to mass spectra classification
Publication TypeConference Paper
Year of Publication2012
AuthorsBellón V, Cerquides J, Grosse I
Conference Name6th European Workshop on Probabilistic Graphical Models
EditorialDECSAI, University of Granada
Conference LocationGranada
Paginación19-26
Date Published19/09/2012
ISBN Number978-84-15536-57-4
Resumen

Classi?ers based on probabilistic graphical models are very e?ective. In continuous domains, parameters for those classi?ers are usually adjusted by maximum likelihood. When data is scarce, this can easily lead to over?tting. Nowadays, models are sought in domains where the number of data items is small and the number of variables is large. This is particularly true in the realm of bioinformatics. In this work we introduce Gaussian Join Trees (GJT) classi?ers to try to partially overcome this issue by performing exact bayesian model averaging over the parameters. We use two di?erent mass spectra classi?cation datasets for cancer prediction to compare GJT classi?ers with those learnt by maximum likelihood.

URLhttp://leo.ugr.es/pgm2012/proceedings/proceedings.pdf