An Alliance for Uncertainty Quantification: Type-2 Fuzzy Logic Systems With Conformal Prediction

Uncertainty Quantification (UQ) plays a crucial role in high-risk applications, where reliable and precise Predictive Intervals (PIs) are essential for informed decision-making. This study introduces an Alliance for Uncertainty Quantification (UQ) that integrates IT2-FLSs with Conformal Prediction (CP) to generate High-Quality (HQ) PIs. The Alliance leverages the inherent ability of IT2-FLSs to model uncertainties through their structure, combined with CP as an additional layer to provide statistical guarantees that the generated PIs achieve marginal coverage for a desired confidence level. We describe three CP methods used in the Alliance to transform the PIs of any IT2-FLS into HQ-PIs, ensuring tightness and compactness of the PIs. We investigate the synergy between the CP methods and the IT2-FLS structure, particularly the Center of Sets Calculation Methods (CSCM), and demonstrate that the choice of both the CP method and CSCM significantly impacts the performance of the Alliance. We validate our proposed framework on publicly available datasets. The comparative results indicate that the Alliance outperforms standalone IT2-FLS and CP models. The results of the paper underscore the effectiveness of the Alliance in accurately quantifying uncertainties and generating HQ-PIs, making it a robust solution for high-risk applications requiring reliable and precise predictions.