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  • Book
  • Open Access
  • © 2023

Learning to Quantify

  • Introduces learning to quantify by looking at the supervised learning methods used to perform it

  • Details evaluation measures and protocols to be used for evaluating the quality of the returned predictions

  • Suitable for researchers, data scientists, or PhD students in information retrieval or applied data science

  • This book is open access, which means that you have free and unlimited access

Part of the book series: The Information Retrieval Series (INRE, volume 47)

About this book

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 by means of supervised learning. In data science, learning to quantify is a task of its own 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 book introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research.

The book is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate (“macro”) data rather than on individual (“micro”) data.



Keywords

  • Information Retrieval
  • Machine Learning
  • Supervised Learning
  • Data Mining
  • Prevalence Estimation
  • Class Prior Estimation
  • Data Science
  • Open Access

Authors and Affiliations

  • Institute of Information Science and Technology, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Andrea Esuli, Fabrizio Sebastiani

  • Dipartimento di Ingegneria dell'Informazione, University of Padua, Padova, Italy

    Alessandro Fabris

  • Consiglio Nazionale delle Ricerche, Institute of Information Science and Technology, Pisa, Italy

    Alejandro Moreo

About the authors

Andrea Esuli is a tenured Senior Researcher at the Italian National Council of Research. His research interests include learning to quantify, deep learning for text analysis, cross-modal classification, technology-assisted review, and representation learning.

Alessandro Fabris is a PhD student at the University of Padova. His research interests include learning to quantify, and the fairness and bias of retrieval and classification systems.

Alejandro Moreo is a tenured Researcher at the Italian National Council of Research. His research interests include learning to quantify, deep learning for text analysis, cross-lingual text classification, authorship analysis, and representation learning.

Fabrizio Sebastiani is a tenured Director of Research at the Italian National Council of Research. His research interests include learning to quantify, cross-lingual text classification, technology-assisted review, authorship analysis, and representation learning.


Bibliographic Information