Document - Learning to quantify: Estimating class prevalence via supervised learning

2019

Contribution to conference Open Access

Learning to quantify: Estimating class prevalence via supervised learning

Moreo Fernandez A. D., Sebastiani F.

Class Prior Estimation Tutorial Supervised Prevalence Estimation Text Quantification

Quantification (also known as "supervised prevalence estimation", or" class prior estimation") is the task of estimating, given a set ? of unlabelled items and a set of classes C= c1,..., c| C|, the relative frequency (or" prevalence") p (ci) of each class ci C, ie, the fraction of items in ? that belong to ci. The goal of this course is to introduce the audience to the problem of quantification and to its importance, to the main supervised learning techniques that have been proposed for solving it, to the metrics used to evaluate them, and to what appear to be the most promising directions for further research.

Source: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1415–1416, Paris, France, 21-25/06/2019

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@inproceedings{oai:it.cnr:prodotti:415592,
	title = {Learning to quantify: Estimating class prevalence via supervised learning},
	author = {Moreo Fernandez A. D. and Sebastiani F.},
	doi = {10.1145/3331184.3331389},
	booktitle = {42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1415–1416, Paris, France, 21-25/06/2019},
	year = {2019}
}