Abstract
In the Probabilistic Graphical Model (PGM) community there is an interest around tractable models, i.e., those that can guarantee exact inference even at the price of expressiveness. Structure learning algorithms are interesting tools to automatically infer both these architectures and their parameters from data. Even if the resulting models are efficient at inference time, learning them can be very slow in practice. Here we focus on Cutset Networks (CNets), a recently introduced tractable PGM representing weighted probabilistic model trees with tree-structured models as leaves. CNets have been shown to be easy to learn, and yet fairly accurate. We propose a learning algorithm that aims to improve their average test log-likelihood while preserving efficiency during learning by adopting a random forest approach. We combine more CNets, learned in a generative Bayesian framework, into a generative mixture model. A thorough empirical comparison on real word datasets, against the original learning algorithms extended to our ensembling approach, proves the validity of our approach.
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Notes
- 1.
Source code is available at http://www.di.uniba.it/~ndm/dcsn/.
- 2.
All experiments have been run on a 4-core Intel Xeon E312xx (Sandy Bridge) @2.0 GHz with 8 Gb of RAM and Ubuntu 14.04.1, kernel 3.13.0-39.
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Acknowledgements
Work supported by the project PUGLIA@SERVICE (PON02 00563 3489339) financed by the Italian Ministry of University and Research (MIUR) and by the European Commission through the project MAESTRA, grant no. ICT-2013-612944.
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Di Mauro, N., Vergari, A., Basile, T.M.A. (2015). Learning Bayesian Random Cutset Forests. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_13
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