2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2026
No evaluation without fair representation : Impact of label and selection bias on the evaluation, performance and mitigation of classification models
Magali Legast, Toon Calders, François Fouss
Bias can be introduced in diverse ways in machine learning datasets, for example via selection or label bias. Although these bias types in themselves have an influence on important…
cs.LG2025★ 2 cited
Interpretable and Fair Mechanisms for Abstaining Classifiers
Daphne Lenders, Andrea Pugnana, Roberto Pellungrini +3
Abstaining classifiers have the option to refrain from providing a prediction for instances that are difficult to classify. The abstention mechanism is designed to trade off the cl…