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Abstract: A sensible use of classifiers must be based on the estimated reliability of their predictions. A cautious classifier would delegate the difficult or uncertain predictions to other, possibly more specialised, classifiers. In this paper we analyse and develop this idea of delegating classifiers in a systematic way. First, we design a two-step scenario where a first classifier chooses which examples to classify and delegates the difficult examples to train a second classifier. Secondly,... (Update)
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BibTeX entry: (Update)
Ferri, C., Flach, P., & Hernandez-Orallo, J. (2004). Delegating classifiers. Proceedings of 21th International Conference on Machine Leaning (ICML'04) (pp. 106--110). Alberta, Canada: Omnipress. http://citeseer.ist.psu.edu/ferri04delegating.html More
@misc{ ferri04delegating,
author = "C. Ferri and P. Flach and J. Hernandez-Orallo",
title = "Delegating classifiers",
text = "Ferri, C., Flach, P., & Hernandez-Orallo, J. (2004). Delegating classifiers.
Proceedings of 21th International Conference on Machine Leaning (ICML'04)
(pp. 106--110). Alberta, Canada: Omnipress.",
year = "2004",
url = "citeseer.ist.psu.edu/ferri04delegating.html" }
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