Crowd-Powered Hybrid Classification Services
Calibration is all you need
Burcu Sayin (Università degli Studi di Trento)
Evgeny Krivosheev (Università degli Studi di Trento)
Jorge Ramirez (Università degli Studi di Trento)
Fabio Casati (Università degli Studi di Trento)
Ekaterina Taran (TPU)
Veronika Malanina (TPU)
Jie Yang (TU Delft - Web Information Systems)
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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.
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