@article{Alnemari2026, 
author = {Mohammed Hassan Alnemari and Abdelrahman Osman Elfaki and Anas Bushnag and Mohamed Hussien Mohamed Nerma},
title = {Conformal Prediction for Reliable Hyperparameter Selection in Sparse FIR Filter Design},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {3},
pages = {57},
keywords = {Conformal prediction, sparse FIR filter, edge IoT, surrogate modeling, uncertainty quantification, hyperparameter optimization},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.082976},
doi = {10.32604/cmc.2026.082976},
abstract = {Sparse finite impulse response (FIR) filters reduce computational cost on resource-constrained devices, but selecting the sparsification threshold  λ is typically left to grid search or hand tuning. We propose a two-stage method: a 67,331-parameter surrogate network predicts  (Ap,As,S) (passband ripple in dB, stopband attenuation in dB, sparsity in %) from a filter specification and a candidate  λ, and split conformal prediction (CP) calibrates ± intervals around each prediction. We then select  λ by minimizing a worst-case penalty computed on the conservative ends of the intervals (the upper bound on  Ap and the lower bound on  As). On 10,000 test specifications the method reaches 76.5% specification satisfaction, near-parity with grid search (78.4%) with a 1.9 × speedup, while point-prediction surrogates reach only 39.4%. On feasible specifications (where any grid  λ satisfies both constraints), the method reaches 97.6%. Stratified (Mondrian) conformal prediction lifts standard CP coverage from 67%–75% to 95.5%, and adaptive recalibration brings passband coverage to 91.3%. The procedure transfers without modification to iteratively reweighted least squares (IRLS) sparsification (76.6%) and to highpass (79.2%) and bandpass (52.4%) filters. The implementation runs on a central processing unit (CPU) and is suitable for edge deployment; code and data are public.}
}