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.

• Type: Journal
• Year: 2006
• Where: PAMI
• Authors: Guillaume Bouchard, Gilles Celeux
• Keywords: Bayesian Entropy Criterion, Classification Error Rate, Model, PAMI 2006