Tractability of most probable explanations in multidimensional Bayesian network classifiers

Benjumeda Barquita, Marco Alberto and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María (2018). Tractability of most probable explanations in multidimensional Bayesian network classifiers. "International Journal of Approximate Reasoning", v. 93 ; pp. 74-87. ISSN 0888-613X. https://doi.org/10.1016/j.ijar.2017.10.024.

Description

Title: Tractability of most probable explanations in multidimensional Bayesian network classifiers
Author/s:
  • Benjumeda Barquita, Marco Alberto
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
Item Type: Article
Título de Revista/Publicación: International Journal of Approximate Reasoning
Date: February 2018
ISSN: 0888-613X
Volume: 93
Subjects:
Freetext Keywords: Multidimensional classification; Bayesian network classifiers; Most probable explanation complexity; Machine Learning
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Multidimensional Bayesian network classifiers have gained popularity over the last few years due to their expressive power and their intuitive graphical representation. A drawback of this approach is that their use to perform multidimensional classification, a generalization of multi-label classification, can be very computationally demanding when there are a large number of class variables. Thus, a key challenge in this field is to ensure the tractability of these models during the learning process. In this paper, we show how information about the most common queries of multidimensional Bayesian network classifiers affects the complexity of these models. We provide upper bounds for the complexity of the most probable explanations and marginals of class variables conditioned to an instantiation of all feature variables. We use these bounds to propose efficient strategies for bounding the complexity of multidimensional Bayesian network classifiers during the learning process, and provide a simple learning method with an order-based search that guarantees the tractability of the returned models. Experimental results show that our approach is competitive with other methods in the state of the art and also ensures the tractability of the learned models.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain Proyect
Government of SpainTIN2016-79684-PUnspecifiedUniversidad Politécnica de MadridAvances en clasificación multidimensional y detección de anomalías con redes bayesianas
Madrid Regional GovernmentS2013/ICE-2845CASI-CAMUnspecifiedConceptos y Aplicaciones de los Sistemas Inteligentes
Horizon 2020720270HBP SGA1UnspecifiedHuman Brain Project Specific Grant Agreement 1

More information

Item ID: 54551
DC Identifier: http://oa.upm.es/54551/
OAI Identifier: oai:oa.upm.es:54551
DOI: 10.1016/j.ijar.2017.10.024
Official URL: http://isiarticles.com/bundles/Article/pre/pdf/113742.pdf
Deposited by: Memoria Investigacion
Deposited on: 25 Apr 2019 06:50
Last Modified: 25 Apr 2019 06:50
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