Multi-Label Classification via Manipulating Labels

DOI

10.2991/iccsee.2013.245How to use a DOI?

Keywords

multi-label, mutual information, pairwise labels

Abstract

Unlike traditional classification problem, multi-label learning task is to predict a label set with unknown size for an example. While the exponential number of possible label sets challenges the task of multi-label learning. Many approaches by manipulating labels have been proposed. In this paper, we propose a new method via manipulating labels for multi-Label Learning: adding a virtual label to the original label set, appending the label subset selected by mutual information for each pairwise labels to the original feature set, and finally learning a binary classifier for each pairwise labels. Extensive experiments show that, compared with advanced multi-label methods, the proposed method induces models with significantly better performance..

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Huaping Guo
AU  - Ming Fan
PY  - 2013/03
DA  - 2013/03
TI  - Multi-Label Classification via Manipulating Labels
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 974
EP  - 977
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.245
DO  - 10.2991/iccsee.2013.245
ID  - Guo2013/03
ER  -