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# A predictive deviance criterion for selecting a generative model in semi-supervised classification

1 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay
Abstract : Semi-supervised classification can be hoped to improve generative classifiers by taking profit of the information provided by the unlabeled data points, especially when there are far more unlabeled data than labeled data. This paper is concerned with selecting a generative classification model from both unlabeled and labeled data. We propose a predictive deviance criterion AIC$_{cond}$ aiming to select a parsimonious and relevant generative classifier in the semi-supervised context. Contrary to standard information criteria as AIC and BIC, AIC$_{cond}$ is focusing to the classification task since it aims to measure the predictive power of a generative model by approximating its predictive deviance. On an other hand, it avoids the computational trouble encountered with cross validation criteria due to the repeated use of the EM algorithm. AIC$_{cond}$ is proved to have consistency properties ensuring its parsimony compared to the Bayesian Entropy Criterion (BEC) which has a similar focus than AIC$_{cond}$. In addition, numerical experiments on both simulated and real data sets highlight an encouraging behavior of AIC$_{cond}$ for variable and model selection in comparison to the other mentioned criteria.
Document type :
Journal articles
Domain :

Cited literature [21 references]

https://hal.inria.fr/inria-00516991
Contributor : Gilles Celeux Connect in order to contact the contributor
Submitted on : Monday, September 13, 2010 - 12:39:14 PM
Last modification on : Sunday, June 26, 2022 - 11:52:13 AM
Long-term archiving on: : Tuesday, December 14, 2010 - 2:44:12 AM

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RR-7377.pdf
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### Identifiers

• HAL Id : inria-00516991, version 1

### Citation

Vincent Vandewalle, Christophe Biernacki, Gilles Celeux, Gérard Govaert. A predictive deviance criterion for selecting a generative model in semi-supervised classification. Computational Statistics and Data Analysis, Elsevier, 2013, 64, pp.220-236. ⟨inria-00516991⟩

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