Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning

Study of data transformation techniques for adapting single-label prototype selection algorithms... •Data transformation methods for instance selection (IS) in multi-label problems is investigated. •The approach allows the adaptation to multi-label of IS methods for single-label. •The adaptation of LSSm algorithm using binary relevance shows competitive results. •At the moment, the IS is not as advantageous for multi-label as it is for single-label. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Expert Systems with Applications Elsevier

Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0957-4174
D.O.I.
10.1016/j.eswa.2018.05.017
Publisher site
See Article on Publisher Site

Abstract

•Data transformation methods for instance selection (IS) in multi-label problems is investigated. •The approach allows the adaptation to multi-label of IS methods for single-label. •The adaptation of LSSm algorithm using binary relevance shows competitive results. •At the moment, the IS is not as advantageous for multi-label as it is for single-label.

Journal

Expert Systems with ApplicationsElsevier

Published: Nov 1, 2018

References

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