Esuli A., Fabris A., Moreo A., Sebastiani F. Learning to Quantify

Springer, 2023. — 145 p. — (The Information Retrieval Series 47). — ISBN: 978-3031204661.This open-access book provides an introduction and an overview of learning to quantify (a.k. a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data using supervised learning. In data science, learning to quantify is a task of its related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate (“biased”) class proportion estimates.The Case for Quantification.
Applications of Quantification.
Evaluation of Quantification Algorithms.
Methods for Learning to Quantify.
Advanced Topics.
The Quantification Landscape.
The Road Ahead.