Best first over-sampling for multilabel classification

Xusheng Ai, Jian Wu, Victor S. Sheng, Yufeng Yao, Pengpeng Zhao, Zhiming Cui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Learning from imbalanced multilabel data is a challenging task. It has attracted considerable attention recently. In this paper we propose a MultiLabel Best First Over-sampling (ML-BFO) to improve the performance of multilabel classification algorithms, based on imbalance minimization and Wilson's ENN rule. Our experimental results show that ML-BFO not only duplicates fewer samples but also reduces the imbalance level much more than two state-of-the-art multilabel sampling methods, i.e., an over-sampling method LP-ROS and an under-sampling method MLeNN. Besides, ML-BFO significantly improves the performance of multilabel classification algorithms, and performs much better than LP-ROS and MLeNN.

Original languageEnglish
Title of host publicationCIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1803-1806
Number of pages4
ISBN (Electronic)9781450337946
DOIs
StatePublished - Oct 17 2015
Event24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia
Duration: Oct 19 2015Oct 23 2015

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
Volume19-23-Oct-2015

Conference

Conference24th ACM International Conference on Information and Knowledge Management, CIKM 2015
CountryAustralia
CityMelbourne
Period10/19/1510/23/15

Keywords

  • Heuristic
  • Multilabel learning
  • Resampling

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  • Cite this

    Ai, X., Wu, J., Sheng, V. S., Yao, Y., Zhao, P., & Cui, Z. (2015). Best first over-sampling for multilabel classification. In CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management (pp. 1803-1806). (International Conference on Information and Knowledge Management, Proceedings; Vol. 19-23-Oct-2015). Association for Computing Machinery. https://doi.org/10.1145/2806416.2806634