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    Learning to Quantify

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    Author(s)
    Esuli, Andrea
    Fabris, Alessandro
    Moreo, Alejandro
    Sebastiani, Fabrizio
    Language
    English
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    Abstract
    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.
    URI
    https://library.oapen.org/handle/20.500.12657/62385
    Keywords
    Information Retrieval; Machine Learning; Supervised Learning; Data Mining; Prevalence Estimation; Class Prior Estimation; Data Science
    DOI
    10.1007/978-3-031-20467-8
    ISBN
    9783031204678, 9783031204661, 9783031204678
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2023
    Grantor
    • Istituto di Scienza e Tecnologie dell'Informazione
    Imprint
    Springer International Publishing
    Series
    The Information Retrieval Series, 47
    Classification
    Information retrieval
    Data mining
    Machine learning
    Pages
    137
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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