Supervised learning with multiple sets of noisy labels presents a complex challenge, arising when several annotators are required to manually label the same training samples, potentially resulting in inconsistencies in class assignments compared to the ground truth. To efficiently learn a classifier in this context, an ensemble approach is developed by leveraging model-based discriminant analysis trained individually on distinct sets of noisy labels. Several strategies are proposed to combine the base learners, extending solutions proposed in the literature for the binary classification setting to the multi-class framework. An application involving the identification of gastrointestinal lesions from colonoscopic videos, revised by seven clinicians, demonstrates the applicability of our proposal.

Cappozzo, A., Learning from Multiple Annotators: An Ensemble Model-Based Classification Approach, in Methodological and Applied Statistics and Demography II, (Bari (Italy)., 17-20 June 2024), Springer, Bari (Italy). 2024:<<ITALIAN STATISTICAL SOCIETY SERIES ON ADVANCES IN STATISTICS>>, 85-89. [10.1007/978-3-031-64350-7_15] [https://hdl.handle.net/10807/310516]

Learning from Multiple Annotators: An Ensemble Model-Based Classification Approach

Cappozzo, Andrea
2024

Abstract

Supervised learning with multiple sets of noisy labels presents a complex challenge, arising when several annotators are required to manually label the same training samples, potentially resulting in inconsistencies in class assignments compared to the ground truth. To efficiently learn a classifier in this context, an ensemble approach is developed by leveraging model-based discriminant analysis trained individually on distinct sets of noisy labels. Several strategies are proposed to combine the base learners, extending solutions proposed in the literature for the binary classification setting to the multi-class framework. An application involving the identification of gastrointestinal lesions from colonoscopic videos, revised by seven clinicians, demonstrates the applicability of our proposal.
2024
Inglese
Methodological and Applied Statistics and Demography II
SIS 2024 - The 52nd Scientific Meeting of the Italian Statistical Society
Bari (Italy).
17-giu-2024
20-giu-2024
9783031643491
Springer
Cappozzo, A., Learning from Multiple Annotators: An Ensemble Model-Based Classification Approach, in Methodological and Applied Statistics and Demography II, (Bari (Italy)., 17-20 June 2024), Springer, Bari (Italy). 2024:<<ITALIAN STATISTICAL SOCIETY SERIES ON ADVANCES IN STATISTICS>>, 85-89. [10.1007/978-3-031-64350-7_15] [https://hdl.handle.net/10807/310516]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/310516
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