Crowd-Powered Hybrid Classification Services

Calibration is all you need

Conference Paper (2021)
Author(s)

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)

Research Group
Web Information Systems
DOI related publication
https://doi.org/10.1109/ICWS53863.2021.00019
More Info
expand_more
Publication Year
2021
Language
English
Research Group
Web Information Systems
Pages (from-to)
42-50
ISBN (print)
978-1-6654-1682-5
ISBN (electronic)
978-1-6654-1681-8

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.

No files available

Metadata only record. There are no files for this record.