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oapen-20.500.12657-623852024-03-28T08:18:33Z Learning to Quantify Esuli, Andrea Fabris, Alessandro Moreo, Alejandro Sebastiani, Fabrizio Information Retrieval Machine Learning Supervised Learning Data Mining Prevalence Estimation Class Prior Estimation Data Science thema EDItEUR::U Computing and Information Technology::UN Databases::UNH Information retrieval thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning 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. 2023-04-13T14:03:24Z 2023-04-13T14:03:24Z 2023 book ONIX_20230413_9783031204678_16 9783031204678 9783031204661 https://library.oapen.org/handle/20.500.12657/62385 eng The Information Retrieval Series application/pdf n/a 978-3-031-20467-8.pdf https://link.springer.com/978-3-031-20467-8 Springer Nature Springer International Publishing 10.1007/978-3-031-20467-8 10.1007/978-3-031-20467-8 6c6992af-b843-4f46-859c-f6e9998e40d5 40ab397c-99db-40fa-8c7a-346e1e19ee16 9783031204678 9783031204661 Springer International Publishing 47 137 Cham Istituto di Scienza e Tecnologie dell'Informazione Institute of Information Science and Technologies open access
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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.
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