PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Model selection in supervised classification
Gilles Celeux and Guillaume Bouchard
In: PAMI (2004), 2004.

Abstract

This article is concerned with the selection of a generative model for supervised classification. Classical model selection criteria are assessing the fit of a model rather than its ability to produce a low classification error rate. A new criterion, the so called Bayesian Entropy Criterion (BEC) is proposed. This criterion is taking into account the decisional purpose of a model by minimizing the integrated classification entropy. It provides an interesting alternative to the cross validated error rate which is highly time consuming. The asymptotic behavior of BEC criterion is presented. Numerical experiments on both simulated and real data sets show that BEC is performing better than BIC criterion to select a model minimizing the classification error rate and is providing analogous performances than the cross validated error rate.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:662
Deposited By:Michele Sebag
Deposited On:29 December 2004