Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification

Varando, Gherardo and Bielza Lozoya, Maria Concepcion and Larrañaga Múgica, Pedro (2014). Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification. In: "7th European Workshop, PGM 2014", 17-19 Sep 2014, Utrecht, Holanda. ISBN 978-3-319-11433-0. pp. 519-534.

Description

Title: Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification
Author/s:
  • Varando, Gherardo
  • Bielza Lozoya, Maria Concepcion
  • Larrañaga Múgica, Pedro
Item Type: Presentation at Congress or Conference (Unspecified)
Event Title: 7th European Workshop, PGM 2014
Event Dates: 17-19 Sep 2014
Event Location: Utrecht, Holanda
Title of Book: Probabilistic Graphical Models
Date: 2014
ISBN: 978-3-319-11433-0
Volume: 8754
Subjects:
Freetext Keywords: Bayesian network classifier; Multi-label classification; Expressive power; Chain classifier; Binary relevance
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

Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of h binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the binary relevance method and Bayesian network chain classifiers. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.

More information

Item ID: 35334
DC Identifier: http://oa.upm.es/35334/
OAI Identifier: oai:oa.upm.es:35334
Official URL: http://link.springer.com/chapter/10.1007/978-3-319-11433-0_34
Deposited by: Memoria Investigacion
Deposited on: 27 May 2015 11:28
Last Modified: 17 Nov 2017 09:08
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