Hybrid classification services are online services that combine machine learning (ML) and humans - either crowd workers or experts - to achieve a classification objective, from relatively simple ones such as deriving the sentiment of a text to more complex ones such as medical diagnoses. This paper takes the first steps toward a science for hybrid classification services, discussing key concepts, challenges, and architectures, and then focusing on a central aspect, that of ML calibration and how it can be achieved with crowdsourced labels.

Crowd-Powered Hybrid Classification Services: Calibration is all you need / Sayin, B.; Krivosheev, E.; Ramirez, J.; Casati, F.; Taran, E.; Malanina, V.; Yang, J.. - ELETTRONICO. - (2021), pp. 42-50. (Intervento presentato al convegno 2021 IEEE International Conference on Web Services, ICWS 2021 tenutosi a Chicago, IL, USA nel 11 November, 2021) [10.1109/ICWS53863.2021.00019].

Crowd-Powered Hybrid Classification Services: Calibration is all you need

Krivosheev E.;Casati F.;
2021-01-01

Abstract

Hybrid classification services are online services that combine machine learning (ML) and humans - either crowd workers or experts - to achieve a classification objective, from relatively simple ones such as deriving the sentiment of a text to more complex ones such as medical diagnoses. This paper takes the first steps toward a science for hybrid classification services, discussing key concepts, challenges, and architectures, and then focusing on a central aspect, that of ML calibration and how it can be achieved with crowdsourced labels.
2021
Proceedings - 2021 IEEE International Conference on Web Services, ICWS 2021
Chicago, IL, USA
Institute of Electrical and Electronics Engineers Inc.
978-1-6654-1681-8
Sayin, B.; Krivosheev, E.; Ramirez, J.; Casati, F.; Taran, E.; Malanina, V.; Yang, J.
Crowd-Powered Hybrid Classification Services: Calibration is all you need / Sayin, B.; Krivosheev, E.; Ramirez, J.; Casati, F.; Taran, E.; Malanina, V.; Yang, J.. - ELETTRONICO. - (2021), pp. 42-50. (Intervento presentato al convegno 2021 IEEE International Conference on Web Services, ICWS 2021 tenutosi a Chicago, IL, USA nel 11 November, 2021) [10.1109/ICWS53863.2021.00019].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/349819
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