Pseudo labels for imbalanced multi-label learning

Abstract

The classification with instances which can be tagged with any of the 2<sup>L</sup> possible subsets from the predefined L labels is called multi-label classification. Multi-label classification is commonly applied in domains, such as multimedia, text, web and biological data analysis. The main challenge lying in multi-label classification is the dilemma of… (More)
DOI: 10.1109/DSAA.2014.7058047

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Cite this paper

@article{Zeng2014PseudoLF, title={Pseudo labels for imbalanced multi-label learning}, author={Wenrong Zeng and Xue-wen Chen and Hong Cheng}, journal={2014 International Conference on Data Science and Advanced Analytics (DSAA)}, year={2014}, pages={25-31} }