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Córdoba Sánchez, Irene and Garrido Merchán, Eduardo César and Hernández Lobato, Daniel and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María (2018). Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks. In: "18th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2018)", 23-26 Oct 2018, Granada, España. ISBN 978-3-030-00373-9. pp. 44-54. https://doi.org/10.1007/978-3-030-00374-6_5.
Title: | Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks |
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Author/s: |
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Item Type: | Presentation at Congress or Conference (Article) |
Event Title: | 18th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2018) |
Event Dates: | 23-26 Oct 2018 |
Event Location: | Granada, España |
Title of Book: | Advances in Artificial Intelligence |
Date: | 2018 |
ISBN: | 978-3-030-00373-9 |
Subjects: | |
Faculty: | E.T.S. de Ingenieros Informáticos (UPM) |
Department: | Inteligencia Artificial |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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The PC algorithm is a popular method for learning the structure of Gaussian Bayesian networks. It carries out statistical tests to determine absent edges in the network. It is hence governed by two parameters: (i) The type of test, and (ii) its significance level. These parameters are usually set to values recommended by an expert. Nevertheless, such an approach can suffer from human bias, leading to suboptimal reconstruction results. In this paper we consider a more principled approach for choosing these parameters in an automatic way. For this we optimize a reconstruction score evaluated on a set of different Gaussian Bayesian networks. This objective is expensive to evaluate and lacks a closed-form expression, which means that Bayesian optimization (BO) is a natural choice. BO methods use a model to guide the search and are hence able to exploit smoothness properties of the objective surface. We show that the parameters found by a BO method outperform those found by a random search strategy and the expert recommendation. Importantly, we have found that an often overlooked statistical test provides the best over-all reconstruction results.
Type | Code | Acronym | Leader | Title |
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Madrid Regional Government | S2013/ICE-2845 | CASI – CAM | Unspecified | Conceptos y aplicaciones de los sistemas inteligentes |
Government of Spain | TIN2016-79684-P | Unspecified | Universidad Politécnica de Madrid | Avances en clasificación multidimensional y detección de anomalías con redes bayesianas |
Government of Spain | TIN2016-76406-P | Unspecified | Universidad Autónoma de Madrid | Fronteras en aprendizaje automático y aplicaciones multidisciplinares |
Government of Spain | TEC2016-81900-REDT | Unspecified | Universidad de Valencia | Avances en métodos núcleo para datos estructurados |
Government of Spain | C080020-09 | Unspecified | Unspecified | Cajal Blue Brain project |
Item ID: | 54649 |
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DC Identifier: | http://oa.upm.es/54649/ |
OAI Identifier: | oai:oa.upm.es:54649 |
DOI: | 10.1007/978-3-030-00374-6_5 |
Official URL: | https://link.springer.com/content/pdf/10.1007%2F978-3-030-00374-6.pdf |
Deposited by: | Memoria Investigacion |
Deposited on: | 29 Apr 2019 10:30 |
Last Modified: | 29 Apr 2019 10:30 |