Abstract
Multilabel classification is a task that has been broadly studied in late years. However, how to face learning from imbalanced multilabel datasets (MLDs) has only been addressed latterly. In this regard, a few proposals can be found in the literature, most of them based on resampling techniques adapted from the traditional classification field. The success of these methods varies extraordinarily depending on the traits of the chosen MLDs.
One of the characteristics which significantly influences the behavior of multilabel resampling algorithms is the joint appearance of minority and majority labels in the same instances. It was demonstrated that MLDs with a high level of concurrence among imbalanced labels could hardly benefit from resampling methods. This paper proposes an original resampling algorithm, called REMEDIAL, which is not based on removing majority instances nor creating minority ones, but on a procedure to decouple highly imbalanced labels. As will be experimentally demonstrated, this is an interesting approach for certain MLDs.
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Notes
- 1.
Visualizing all label interactions in an MLD is, in some cases, almost impossible due to the large number of labels. For that reason, only the most frequent labels and the most rare ones for each MLD are represented in these plots. High resolution version of these plots can be found at http://simidat.ujaen.es/remedial and they can be generated using the mldr R package [32].
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Acknowledgments
F. Charte is supported by the Spanish Ministry of Education under the FPU National Program (Ref. AP2010-0068). This work was partially supported by the Spanish Ministry of Science and Technology under projects TIN2011-28488 and TIN2012-33856, and the Andalusian regional projects P10-TIC-06858 and P11-TIC-7765.
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Charte, F., Rivera, A., del Jesus, M.J., Herrera, F. (2015). Resampling Multilabel Datasets by Decoupling Highly Imbalanced Labels. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_41
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