A hybrid statistical baseline plus data-driven residual learner framework is proposed to calibrate decision risk for process capability indices under finite-sample uncertainty, showing better stability than conventional thresholding in near-boundary cases.
Reliable classification: Learning classifiers that distinguish aleatoricandepistemicuncertainty.Information Sci- ences, 255:16–29, 2014
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A Machine Learning Framework for Uncertainty-Calibrated Capability Decision under Finite Samples
A hybrid statistical baseline plus data-driven residual learner framework is proposed to calibrate decision risk for process capability indices under finite-sample uncertainty, showing better stability than conventional thresholding in near-boundary cases.