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  Multi-Label Learning by Exploiting Label Dependency

Zhang, M.-L., & Zhang, K. (2010). Multi-Label Learning by Exploiting Label Dependency. In B. Rao, B. Krishnapuram, A. Tomkins, & Q. Yang (Eds.), 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010) (pp. 999-1008). New York, NY, USA: ACM Press.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BF42-5 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-81A7-F
Genre: Konferenzbeitrag

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externe Referenz:
https://dl.acm.org/citation.cfm?doid=1835804.1835930 (Verlagsversion)
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Urheber

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 Urheber:
Zhang, M-L, Autor
Zhang, K1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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Schlagwörter: -
 Zusammenfassung: In multi-label learning, each training example is associated
with a set of labels and the task is to predict the proper label
set for the unseen example. Due to the tremendous (exponential)
number of possible label sets, the task of learning
from multi-label examples is rather challenging. Therefore,
the key to successful multi-label learning is how to effectively
exploit correlations between different labels to facilitate
the learning process. In this paper, we propose to use a
Bayesian network structure to efficiently encode the condi-
tional dependencies of the labels as well as the feature set,
with the feature set as the common parent of all labels. To
make it practical, we give an approximate yet efficient procedure
to find such a network structure. With the help of this
network, multi-label learning is decomposed into a series of
single-label classification problems, where a classifier is constructed
for each label by incorporating its parental labels
as additional features. Label sets of unseen examples are
predicted recursively according to the label ordering given
by the network. Extensive experiments on a broad range of
data sets validate the effectiveness of our approach against
other well-established methods.

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Sprache(n):
 Datum: 2010-07
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1145/1835804.1835930
BibTex Citekey: 6631
 Art des Abschluß: -

Veranstaltung

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Titel: 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010)
Veranstaltungsort: Washington, DC, USA
Start-/Enddatum: 2010-07-25 - 2010-07-28

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Quelle 1

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Titel: 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010)
Genre der Quelle: Konferenzband
 Urheber:
Rao, B, Herausgeber
Krishnapuram, B, Herausgeber
Tomkins, A, Herausgeber
Yang, Q, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: New York, NY, USA : ACM Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 999 - 1008 Identifikator: ISBN: 978-1-4503-0055-1