Data Sheet 1_Leakage-free evaluation of task-aware Nyström sampling for IoMT intrusion detection.pdf

We propose a task-aware, importance-weighted Nyström landmark-selection mechanism that scores each training sample's classifier uncertainty, boundary distance, representativeness, and minority-class importance, with an optional quantum-amplitude reparameterization (QI-AWNS) shown to be algebraically equivalent to direct score-proportional sampling; the contribution is the importance scoring and its rigorous evaluation, not the amplitude framing or a quantum advantage.

We benchmark QI-AWNS against uniform, K-means, and approximate ridge-leverage-score Nyström sampling, five tabular baselines, and a full-kernel RBF-SVM on two leakage-free IoMT benchmarks (ECU-IoHT, WUSTL-EHMS-2020) under a strict split-before-fit protocol, with each test partition evaluated once. Across 10 reseeded splits, per-split retuning of the importance weights improves QI-AWNS's stability relative to a frozen-weight configuration and yields a mean balanced-accuracy gain of 0.18 points over uniform sampling on ECU-IoHT.