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This paper attempts multi-label classification by extending the idea of independent binary classification models for each output label, and exploring how the inherent correlation between output labels can be used to improve predictions.
Tsoumakas, Grigorios; Katakis, Ioannis (2007). ”Multi-label classification: an overview” . International Journal of Data Warehousing and Mining 3 (3): 1–13 doi:104018/jdwm.2007070101
2007
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011
2011
Earlier work this paper cites.
2012
Cited alongside, same era.
cs.stanford.edu/people/adityaj/cs229_fall2014_dataset.html
Cited in the paper.
miscsnapnets, author = Jure Leskovec and Andrej Krevl, title = SNAP Datasets: Stanford Large Network Dataset Collection, howpublished = http://snap.stanford.edu/data , month = jun, year = 2014
2014
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