A novel score function for conformal prediction in rule-based binary classification
Sara Narteni, Alberto Carlevaro, Fabrizio Dabbene, Marco Muselli, Maurizio Mongelli · Pattern Recognition · 2025
• Performance guarantees for rule-based classification models. • Novel score function accounting for rule overlaps via geometrical rule similarity. • Conformal prediction-guided tuning of rules via conformal critical set. Computer scientists consider an artificial intelligence system safe and trustworthy if it fulfills four pillars: robustness, transparency, fairness, and privacy. We propose a fifth fundamental aspect: conformal guarantee, that is, the probabilistic assurance that the system will behave as expected. We introduce CONFIDERAI (Conformal Interpretable by Design Explainable and Reliable Artificial Intelligence), a new score function for binary rule-based classifiers depending on both rules’ performance and geometry; the latter includes both the position of points within rule boundaries and rule overlaps, these being quantified via geometrical rule similarity. Furthermore, we address the problem of individuating regions in the feature space in which conformal guarantees are satisfied, by defining the concept of conformal critical set (CCS). The overall method is tested with promising results, comparable in efficiency to traditional scores, on ten datasets of real-world interest, such as domain name server tunneling detection and cardiovascular disease prediction. Moreover, newly generated rules from CCS resulted into an improved precision and reduced error on a target class, thus avoiding prediction failures in safety-critical contexts.