Efficient Monte Carlo methods for multi-dimensional learning with classifier chains

Luengo García, David; Read, Jesse y Martino, Luca (2014). Efficient Monte Carlo methods for multi-dimensional learning with classifier chains. "Pattern Recognition", v. 47 (n. 3); pp. 1535-1546. ISSN 0031-3203. https://doi.org/10.1016/j.patcog.2013.10.006.

Descripción

Título: Efficient Monte Carlo methods for multi-dimensional learning with classifier chains
Autor/es:
  • Luengo García, David
  • Read, Jesse
  • Martino, Luca
Tipo de Documento: Artículo
Título de Revista/Publicación: Pattern Recognition
Fecha: Marzo 2014
Volumen: 47
Materias:
Palabras Clave Informales: Classifier chains; Multi-dimensional classification; Multi-label classification; Monte Carlo methods; Bayesian inference
Escuela: E.T.S.I. y Sistemas de Telecomunicación (UPM)
Departamento: Teoría de la Señal y Comunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.

Más información

ID de Registro: 35934
Identificador DC: http://oa.upm.es/35934/
Identificador OAI: oai:oa.upm.es:35934
Identificador DOI: 10.1016/j.patcog.2013.10.006
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0031320313004160
Depositado por: Memoria Investigacion
Depositado el: 12 Feb 2016 17:20
Ultima Modificación: 31 Mar 2016 22:56
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