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Multi-label classification and extracting predicted class hierarchies

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2011

Autor:innen

Brucker, Florian

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http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-126121
DOI (zitierfähiger Link)
https://doi.org/10.1016/j.patcog.2010.09.010
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Open Access-Veröffentlichung

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Pattern Recognition. 2011, 44(3), pp. 724-738. ISSN 0031-3203. Available under: doi: 10.1016/j.patcog.2010.09.010

Zusammenfassung

This paper investigates hierarchy extraction from results of multi-label classification (MC). MC deals with instances labeled by multiple classes rather than just one, and the classes are often hierarchically organized. Usually multi-label classifiers rely on a predefined class hierarchy. A much less investigated approach is to suppose that the hierarchy is unknown and to infer it automatically. In this setting, the proposed system classifies multi-label data and extracts a class hierarchy from multi-label predictions. It is based on a combination of a novel multi-label extension of the fuzzy Adaptive Resonance Associative Map (ARAM) neural network with an association rule learner.

Zusammenfassung in einer weiteren Sprache

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004 Informatik

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Multi-label, Adaptive Resonance Theory, Document Classification, Hierarchy Extraction

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ISO 690BRUCKER, Florian, Fernando BENITES, Elena SAPOZHNIKOVA, 2011. Multi-label classification and extracting predicted class hierarchies. In: Pattern Recognition. 2011, 44(3), pp. 724-738. ISSN 0031-3203. Available under: doi: 10.1016/j.patcog.2010.09.010
BibTex
@article{Brucker2011Multi-2997,
  year={2011},
  doi={10.1016/j.patcog.2010.09.010},
  title={Multi-label classification and extracting predicted class hierarchies},
  number={3},
  volume={44},
  issn={0031-3203},
  journal={Pattern Recognition},
  pages={724--738},
  author={Brucker, Florian and Benites, Fernando and Sapozhnikova, Elena}
}
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