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Model selection in supervised classification

Guillaume Bouchard 1 Gilles Celeux 1
1 SELECT - Model selection in statistical learning
LMO - Laboratoire de Mathématiques d'Orsay, Inria Saclay - Ile de France
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.
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Submitted on : Friday, May 19, 2006 - 9:00:55 PM
Last modification on : Wednesday, April 20, 2022 - 3:37:37 AM
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  • HAL Id : inria-00070612, version 1


Guillaume Bouchard, Gilles Celeux. Model selection in supervised classification. [Research Report] RR-5391, INRIA. 2004, pp.22. ⟨inria-00070612⟩



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