Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration Strategy
This addresses the problem of deploying reliable deep learning models in high-stakes applications by improving uncertainty quantification, though it is incremental as it builds on existing GT2-FLS methods.
The study tackled the computational inefficiency and lack of adaptability in General Type-2 Fuzzy Logic Systems (GT2-FLSs) for uncertainty quantification by proposing a blueprint calibration strategy, which enables efficient adaptation to new coverage levels without retraining and achieves superior performance on high-dimensional datasets.
Uncertainty Quantification (UQ) is crucial for deploying reliable Deep Learning (DL) models in high-stakes applications. Recently, General Type-2 Fuzzy Logic Systems (GT2-FLSs) have been proven to be effective for UQ, offering Prediction Intervals (PIs) to capture uncertainty. However, existing methods often struggle with computational efficiency and adaptability, as generating PIs for new coverage levels $(φ_d)$ typically requires retraining the model. Moreover, methods that directly estimate the entire conditional distribution for UQ are computationally expensive, limiting their scalability in real-world scenarios. This study addresses these challenges by proposing a blueprint calibration strategy for GT2-FLSs, enabling efficient adaptation to any desired $φ_d$ without retraining. By exploring the relationship between $α$-plane type reduced sets and uncertainty coverage, we develop two calibration methods: a lookup table-based approach and a derivative-free optimization algorithm. These methods allow GT2-FLSs to produce accurate and reliable PIs while significantly reducing computational overhead. Experimental results on high-dimensional datasets demonstrate that the calibrated GT2-FLS achieves superior performance in UQ, highlighting its potential for scalable and practical applications.