DART: A Discriminative Adaptive Robust Transformation for Geometry-Aware Classification and Road Safety Analytics

Feature preprocessing directly shapes the geometry seen by distance- and similarity-based classifiers. This study introduces DART (Discriminative Adaptive Robust Transformation), a supervised preprocessing method combining median/MAD normalization, bounded nonlinear saturation, and continuous Fisher-mutual-information relevance weighting. DART is compared with supervised feature weighting and metric learning. Across the original conventional-preprocessing 41-dataset benchmark, DART achieved an overall balanced accuracy of 0.8118. In a separate supervised-control experiment, kNN results were NCA 0.8003, FisherWeight 0.7966, DART 0.7960, MIWeight 0.7855, and StandardScaler 0.7742; for RBF-SVM, DART obtained the highest aggregate BA (0.8308). Training-only RBF gamma selection retained a smaller DART advantage (0.8320 vs. 0.8286), and five independent subsamples of the datasets affected by the 20,000-sample cap produced stable conclusions. In US Accidents, default Full DART improved repeated-CV BA from 0.2774 to 0.2906 relative to StandardScaler (paired difference +0.01316; approximate dependence-corrected 95% CI 0.00327–0.02304), while equally budgeted nested k selection yielded 0.3011 versus 0.2797. State-disjoint BA was 0.2814 versus 0.2746 at fixed k and 0.2915 versus 0.2747 for the nested pair. Default Full DART did not rank first among every fixed-setting US control, and rare-class recall remained poor. STATS19 and India likewise show that gains are method-, class-, and validation-dependent; India additionally exhibited a substantial chronological generalization gap. Overall, DART is best interpreted as a competitive supervised geometry transformation whose benefit is classifier- and representation-dependent and comes with additional training cost.