Quantification-oriented learning based on reliable classifiers

@article{Barranquero2015QuantificationorientedLB,
  title={Quantification-oriented learning based on reliable classifiers},
  author={Jos{\'e} Barranquero and Jorge D{\'i}ez and Juan Jos{\'e} del Coz},
  journal={Pattern Recognition},
  year={2015},
  volume={48},
  pages={591-604}
}
Real-world applications demand effective methods to estimate the class distribution of a sample. In many domains, this is more productive than seeking individual predictions. At a first glance, the straightforward conclusion could be that this task, recently identified as quantification, is as simple as counting the predictions of a classifier. However, due to natural distribution changes occurring in real-world problems, this solution is unsatisfactory. Moreover, current quantification models… CONTINUE READING
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Sentiment quantification

  • A. Esuli, F. Sebastiani
  • IEEE Intelligent Systems 25
  • 2010
Highly Influential
6 Excerpts

M

  • K. Bache
  • Lichman, UCI machine learning repository
  • 2013
Highly Influential
3 Excerpts

J

  • J. Barranquero, P. González
  • Dı́ez, J. J. del Coz, On the study of nearest…
  • 2013
2 Excerpts

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