Uncertainty in Artificial Intelligence
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Sequential Model-Based Ensemble Optimization
Alexandre Lacoste, Hugo Larochelle, Mario Marchand, Francois Laviolette
One of the most tedious tasks in the applica- tion of machine learning is model selection, i.e. hyperparameter selection. Fortunately, recent progress has been made in the automation of this process, through the use of sequential model- based optimization (SMBO) methods. This can be used to optimize a cross-validation perfor- mance of a learning algorithm over the value of its hyperparameters. However, it is well known that ensembles of learned models almost consis- tently outperform a single model, even if prop- erly selected. In this paper, we thus propose an extension of SMBO methods that automatically constructs such ensembles. This method builds on a recently proposed ensemble construction paradigm known as Agnostic Bayesian learning. In experiments on 22 regression and 39 classifi- cation data sets, we confirm the success of this proposed approach, which is able to outperform model selection with SMBO.
Pages: 440-448
PS Link:
PDF Link: /papers/14/p440-lacoste.pdf
AUTHOR = "Alexandre Lacoste and Hugo Larochelle and Mario Marchand and Francois Laviolette",
TITLE = "Sequential Model-Based Ensemble Optimization",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "440--448"

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