Abstract
AbstractThis chapter is possibly the central chapter of the book, and looks at the various supervised learning methods for learning to quantify that have been proposed over the years. These methods belong to two main categories, depending on whether they have an aggregative nature (i.e., they require the classification of all individual unlabelled items as an intermediate step) or a non-aggregative nature (i.e., they perform no classification of individual items). In turn, the aggregative methods may be seen as belonging to two main sub-categories, depending on whether the classification of individual unlabelled items is performed by classifiers trained via general-purpose learners or via special-purpose, quantification-oriented learners.
Publisher
Springer International Publishing
Reference165 articles.
1. Alaíz-Rodríguez, R., Guerrero-Curieses, A., and Cid-Sueiro, J. (2011). Class and subclass probability re-estimation to adapt a classifier in the presence of concept drift. Neurocomputing, 74(16):2614–2623.
2. Alexandari, A., Kundaje, A., and Shrikumar, A. (2020). Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), pages 222–232, Vienna, AT.
3. Anderson, T. W. (1962). On the distribution of the two-sample Cramer-von Mises criterion. The Annals of Mathematical Statistics, 33(3):1148–1159.
4. Andrus, M., Spitzer, E., Brown, J., and Xiang, A. (2021). What we can’t measure, we can’t understand: Challenges to demographic data procurement in the pursuit of fairness. In Proceedings of the 4th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2021), pages 249–260, Toronto, CA.
5. Arribas, J. I. and Cid-Sueiro, J. (2005). A model selection algorithm for a posteriori probability estimation with neural networks. IEEE Transactions on Neural Networks, 16(4):799–809.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献