A hybrid geometric classifier using correlation groups and overlap similarities achieves 0.85-0.96 accuracy on standard tabular datasets and 0.85 minority recall on highly imbalanced fraud data via a variational quantum refinement layer.
Is the k-NN Classifier in High Dimensions Affected by the Curse of Dimensionality? Computational Mathematics and Applications
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Quantum-Inspired Geometric Classification with Correlation Group Structures and VQC Decision Modeling
A hybrid geometric classifier using correlation groups and overlap similarities achieves 0.85-0.96 accuracy on standard tabular datasets and 0.85 minority recall on highly imbalanced fraud data via a variational quantum refinement layer.