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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 548312.0, MPI für biologische Kybernetik / Biologische Kybernetik
Multi-Label Learning by Exploiting Label Dependency
Authors:Zhang, M.-L.; Zhang, K.
Editors:Rao, B.; Krishnapuram, B.; Tomkins, A.; Yang, Q.
Date of Publication (YYYY-MM-DD):2010-07
Title of Proceedings:Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010)
Start Page:999
End Page:1008
Physical Description:10
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:6631
URL:http://www.sigkdd.org/kdd2010/
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