Leveraging supervised label dependency propagation for multi-label learning

Bin FU, Guandong XU, Zhihai WANG, Longbing CAO

Research output: Chapter in Book/Report/Conference proceedingChapters

10 Citations (Scopus)

Abstract

Exploiting label dependency is a key challenge in multi-label learning, and current methods solve this problem mainly by training models on the combination of related labels and original features. However, label dependency cannot be exploited dynamically and mutually in this way. Therefore, we propose a novel paradigm of leveraging label dependency in an iterative way. Specifically, each label's prediction will be updated and also propagated to other labels via an random walk with restart process. Meanwhile, the label propagation is implemented as a supervised learning procedure via optimizing a loss function, thus more appropriate label dependency can be learned. Extensive experiments are conducted, and the results demonstrate that our method can achieve considerable improvements in terms of several evaluation metrics. Copyright © 2013 IEEE.

Original languageEnglish
Title of host publicationProceedings of IEEE 13th International Conference on Data Mining, ICDM 2013
Place of PublicationUSA
PublisherIEEE
Pages1061-1066
ISBN (Print)9780768551081
DOIs
Publication statusPublished - 2013

Citation

Fu, B., Xu, G., Wang, Z., & Cao, L. (2013). Leveraging supervised label dependency propagation for multi-label learning. In Proceedings of IEEE 13th International Conference on Data Mining, ICDM 2013 (pp. 1061-1066). IEEE. https://doi.org/10.1109/ICDM.2013.143

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