Abstract
Crowdsourcing is the learning paradigm that aims to combine noisy labels provided by a crowd of human annotators. To facilitate this label fusion, most contemporary crowdsourcing methods assume conditional independence between different annotators. Nevertheless, in many cases this assumption may not hold. This work investigates the effects of groups of correlated annotators in multiclass crowdsourced classification. To deal with this setup, a novel approach is developed to identify groups of dependent annotators via second-order moments of annotator responses. This in turn, enables appropriate dependence aware aggregation of annotator responses. Preliminary tests on synthetic and real data showcase the potential of the proposed approach.
Original language | English (US) |
---|---|
Title of host publication | 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 |
Editors | Michael B. Matthews |
Publisher | IEEE Computer Society |
Pages | 1276-1280 |
Number of pages | 5 |
ISBN (Electronic) | 9781665459068 |
DOIs | |
State | Published - 2022 |
Event | 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 - Virtual, Online, United States Duration: Oct 31 2022 → Nov 2 2022 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
---|---|
Volume | 2022-October |
ISSN (Print) | 1058-6393 |
Conference
Conference | 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 |
---|---|
Country/Territory | United States |
City | Virtual, Online |
Period | 10/31/22 → 11/2/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Classification
- Crowdsourcing
- Ensemble Learning
- Weak supervision