Delegating classifiers

@inproceedings{Ferri2004DelegatingC,
  title={Delegating classifiers},
  author={C{\'e}sar Ferri and Peter A. Flach and Jos{\'e} Hern{\'a}ndez-Orallo},
  booktitle={ICML},
  year={2004}
}
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, we present… CONTINUE READING
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Data Mining: Practical Machine Learning Tools and Techniques

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