Uncertainty Quantification of Text Classification in a Multi-Label Setting for Risk-Sensitive Systems

Jinha Hwang, Carol Gudumotu, Benyamin Ahmadnia

Abstract: This paper addresses the challenge of uncertainty quantification in text classification for medical purposes and provides a three-fold approach to support robust and trustworthy decision-making by medical practitioners. Also, we address the challenge of imbalanced datasets in the medical domain by utilizing the Mondrian Conformal Predictor with a Naïve Bayes classifier.