Stacking Model-Based Classifiers for Dealing With Multiple Sets of Noisy Labels.

ABSTRACT: Supervised learning in presence of multiple sets of noisy labels is a challenging task that is receiving increasing interest in the ever-evolving landscape of healthcare analytics. Such an issue arises when multiple annotators are tasked to manually label the same training samples, potentially giving rise to discrepancies in class assignments among the supplied labels with respect to the ground truth. Commonly, the labeling process is entrusted to a small group of domain experts, and different level of experience and subjectivity may result in noisy training labels. To solve the classification task leveraging on the availability of multiple data annotators, we introduce a novel ensemble methodology constructed combining model-based classifiers separately trained on single sets of noisy lab

SUBMITTER: Montani G 

PROVIDER: S-EPMC11898607 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature