Multi-dimensional classification with super-classes

Read, Jesse; Bielza Lozoya, Maria Concepcion y Larrañaga Múgica, Pedro (2014). Multi-dimensional classification with super-classes. "Ieee Transactions on Knowledge And Data Engineering", v. 26 (n. 7); pp. 1720-1733. ISSN 1041-4347. https://doi.org/10.1109/TKDE.2013.167.

Descripción

Título: Multi-dimensional classification with super-classes
Autor/es:
  • Read, Jesse
  • Bielza Lozoya, Maria Concepcion
  • Larrañaga Múgica, Pedro
Tipo de Documento: Artículo
Título de Revista/Publicación: Ieee Transactions on Knowledge And Data Engineering
Fecha: Julio 2014
Volumen: 26
Materias:
Palabras Clave Informales: Multi-dimensional classification; Problem transformation
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.

Más información

ID de Registro: 35610
Identificador DC: http://oa.upm.es/35610/
Identificador OAI: oai:oa.upm.es:35610
Identificador DOI: 10.1109/TKDE.2013.167
URL Oficial: https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER147-EAC
Depositado por: Memoria Investigacion
Depositado el: 14 Jul 2015 11:30
Ultima Modificación: 17 Nov 2017 09:03
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