3 papers
cs.LG2026
ConformaDecompose: Explaining Uncertainty via Calibration Localization
Fatima Rabia Yapicioglu, Meltem Aksoy, Alberto Rigenti +4
Conformal Prediction provides distribution-free prediction intervals with guaranteed coverage, but its reliance on a single global calibration threshold obscures the sources of unc…
cs.HC2026
Improving Explanations: Applying the Feature Understandability Scale for Cost-Sensitive Feature Selection
Nicola Rossberg, Bennett Kleinberg, Barry O'Sullivan +2
With the growing pervasiveness of artificial intelligence, the ability to explain the inferences made by machine learning models has become increasingly important. Numerous techniq…
cs.AI2025
CORTEX: A Cost-Sensitive Rule and Tree Extraction Method
Marija Kopanja, MiloÅ¡ SaviÄ, Luca Longo
Tree-based and rule-based machine learning models play pivotal roles in explainable artificial intelligence (XAI) due to their unique ability to provide explanations in the form of…