Abstract
A recursive dependent binary relevance model for multi-label classification is proposed where the predicted label of a pattern is obtained in an iterative process. The motivation behind this strategy is the simultaneous intrinsic dependency of the labels and the fact that predicted labels in the final decision by themselves are estimates which can be re-estimated to improve their robustness.
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Rauber, T.W., Mello, L.H., Rocha, V.F., Luchi, D., Varejão, F.M. (2014). Recursive Dependent Binary Relevance Model for Multi-label Classification. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_17
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DOI: https://doi.org/10.1007/978-3-319-12027-0_17
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