Credal Classification through an Ensemble of Confidence-Aware TabTransformers and its Application to Fraud Detection

We address a multi-class classification problem on a tabular dataset comprising both continuous and categorical features, as commonly found in financial and actuarial domains. To achieve robust and reliable predictions, we propose a credal classifier built from an ensemble of augmented probabilistic models, each capable of self-assessing its confidence in the predicted class distribution for a given instance. Each base learner in the ensemble is implemented as a Confidence-Aware TabTransformer: