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Open Access

ARTICLE

Conformal Prediction for Reliable Hyperparameter Selection in Sparse FIR Filter Design

Mohammed Hassan Alnemari1,2,*, Abdelrahman Osman Elfaki3, Anas Bushnag1, Mohamed Hussien Mohamed Nerma1
1 Department of Computer Engineering, Faculty of Computer Science and Information Technology, University of Tabuk, Tabuk, Saudi Arabia
2 AIST Research Center, University of Tabuk, Tabuk, Saudi Arabia
3 Department of Computer Science, Faculty of Computer Science and Information Technology, University of Tabuk, Tabuk, Saudi Arabia
* Corresponding Author: Mohammed Hassan Alnemari. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.082976

Received 07 April 2026; Accepted 04 June 2026; Published online 17 June 2026

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.

Keywords

Conformal prediction; sparse FIR filter; edge IoT; surrogate modeling; uncertainty quantification; hyperparameter optimization

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