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Published Articles >> Table of Contents >> Abstract
18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)
pp. 86-92
Empirical Studies on Multi-label Classification
Tao Li, Florida International Univ, USA
Chengliang Zhang, University of Rochester, USA
Shenghuo Zhu, NEC Laboratories America, USA
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICTAI.2006.55
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| Abstract |
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In classic pattern recognition problems, classes are mutually
exclusive by definition. However, in many applications,
it is quite natural that some instances belong to multiple
classes at the same time. In other words, these applications
are multi-labeled, classes are overlapped by definition
and each instance may be associated to multiple classes.
In this paper, we present a comparative study on various
multi-label approaches using both gene and scene data sets.
We expect our research efforts provide useful insights on
the relationships among various classifiers as well as various
evaluation measures and shed lights on future research.
Although there is no clear winner across various performance
measures, SVM Binary and Multi-label ADTree perform
better than the others on most counts. We then propose
a meta-learning approach by combining SVM binary and
ADTree. Our experiments demonstrate that the combined
method can take the advantages of the single approaches.
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 Additional Information
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Citation:
Tao Li, Chengliang Zhang, Shenghuo Zhu,
"Empirical Studies on Multi-label Classification,"
ictai,
pp. 86-92,
18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06),
2006
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