Efficient Leakage-Free Neural Architecture Search under Leave-One-Subject-Out Evaluation

Abstract:Leave-One-Subject-Out (LOSO) evaluation estimates generalisation performance for subject-based classification but makes Neural Architecture Search (NAS) computationally expensive because a fully nested implementation requires N independent architecture searches and, assuming approximately linear training cost, scales as O(N^2). We propose a leakage-free, block-based approach that shares NAS runs across subjects. On the BioVid Heat Pain dataset, our approach increased the mean accuracy from 82.79% to 83.39% while reducing the number of parameters by up to 99.2%.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.09433 [cs.LG]

(or arXiv:2609.09433v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.09433 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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