An application of SVMs to multi-label classification

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This short application session proposes to use SVMs as a building block for Multi-Label Classification. It serves both as a practice session for the previous class and as an introduction to Multi-Label Classification.

Notebook source
Notebook on Colab

References

J. Read, P. Reutemann, B. Pfahringer, and Geoff Holmes. MEKA: A multi-label/multi-target extension to Weka. Journal of Machine Learning Research, 17(21):1–5, 2016.
J. Read, B. Pfahringer, G. Holmes, and E. Frank. Classifier chains for multi-label classification. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 254–269, 2009.
G. Tsoumakas and I. Katakis. Multi-label classification: An overview. International Journal on Data Warehousing and Mining, 3(3):1–13, 2007.
G. Tsoumakas, I. Katakis, and I. Vlahavas. Mining multi-label data. Data mining and knowledge discovery handbook, pages 667–685. Springer, 2010.
G. Tsoumakas, I. Katakis, and I. Vlahavas. Random k-labelsets for multi-label classification. IEEE Transactions on Knowledge and Data Engineering, 23(7):1079-1089, 2011.