2026-10-12T03:36:44Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34009353 2026-09-28T05:39:01Z category_450 portal_316 item_type_3 month_year_09_2026

Data Sheet 1_Leakage-free evaluation of task-aware Nyström sampling for IoMT intrusion detection.pdf Hiba A. Tarish (25119948) Rosilah Hassan (17542149) Khairul Akram Zainol Ariffin (11731493) Mustafa Musa Jaber (25119951) Knowledge Representation and Machine Learning anomaly detection data leakage imbalanced classification importance sampling Internet of Medical Things intrusion detection landmark selection Nyström kernel approximation <p>Standard Nyström kernel approximation samples landmark points uniformly at random, ignoring that boundary-adjacent and minority-class samples often matter most for imbalanced intrusion detection. 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. This is not significant after Holm correction for multiple comparisons (raw Wilcoxon p = 0.022, adjusted p = 0.129), and no advantage appears over K-means or approximate ridge-leverage-score sampling on either dataset. An 8-variant ablation and weight-search analysis shows that a validation-based ranking of two weight candidates reverses on WUSTL-EHMS-2020's test set. These findings show that task-aware landmark weighting offers limited, dataset-dependent benefit, and that single-split conclusions can overstate it.</p> 2026-09-28T05:39:01Z Dataset Dataset 10.3389/frai.2026.1957727.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Leakage-free_evaluation_of_task-aware_Nystr_m_sampling_for_IoMT_intrusion_detection_pdf/34009353 CC BY 4.0