Publications

Gaussian Join Tree classifiers with applications to mass spectra classification

Publication Type:

Conference Paper

Authors:

Victor Bellón; Jesús Cerquides; Ivo Grosse

Source:

6th European Workshop on Probabilistic Graphical Models, DECSAI, University of Granada, Granada, p.19-26 (2012)

ISBN:

978-84-15536-57-4

URL:

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

Abstract:

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.

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