Selection of Generative Models in Classification

8 years 11 months ago
Selection of Generative Models in Classification
This paper is concerned with the selection of a generative model for supervised classification. Classical criteria for model selection assess the fit of a model rather than its ability to produce a low classification error rate. A new criterion, the Bayesian Entropy Criterion (BEC), is proposed. This criterion takes into account the decisional purpose of a model by minimizing the integrated classification entropy.Itprovidesaninterestingalternativetothecross-validatederrorratewhichiscomputationallyexpensive.Theasymptoticbehavior of the BEC criterion is presented. Numerical experiments on both simulated and real data sets show that BEC performs better than the BIC criterion to select a model minimizing the classification error rate and provides analogous performance to the cross-validated error rate.
Guillaume Bouchard, Gilles Celeux
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PAMI
Authors Guillaume Bouchard, Gilles Celeux
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