Multi-dimensional classification with super-classes

Read, Jesse and Bielza Lozoya, Maria Concepcion and 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.

Description

Title: Multi-dimensional classification with super-classes
Author/s:
  • Read, Jesse
  • Bielza Lozoya, Maria Concepcion
  • Larrañaga Múgica, Pedro
Item Type: Article
Título de Revista/Publicación: Ieee Transactions on Knowledge And Data Engineering
Date: July 2014
ISSN: 1041-4347
Volume: 26
Subjects:
Freetext Keywords: Multi-dimensional classification; Problem transformation
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

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainCSD2008–00010UnspecifiedUnspecifiedUnspecified
Government of SpainTIN2010–20900-C04–04UnspecifiedUnspecifiedUnspecified
Government of Spain2010-CSD2007–00018UnspecifiedUnspecifiedUnspecified

More information

Item ID: 35610
DC Identifier: http://oa.upm.es/35610/
OAI Identifier: oai:oa.upm.es:35610
DOI: 10.1109/TKDE.2013.167
Official URL: https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER147-EAC
Deposited by: Memoria Investigacion
Deposited on: 14 Jul 2015 11:30
Last Modified: 21 May 2019 10:57
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