MDL-GBC constructs class-conditional granular balls by comparing single-ball, two-ball, and core-boundary models under a unified MDL criterion and aggregates them for prediction, achieving the best average accuracy and Macro-F1 on 18 benchmarks.
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Simulated fidelity quantum kernels achieve competitive or better accuracy than RBF kernels on Indian Pines binary and multiclass tasks and Methane Detection data without heavy dimensionality reduction.
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A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length
MDL-GBC constructs class-conditional granular balls by comparing single-ball, two-ball, and core-boundary models under a unified MDL criterion and aggregates them for prediction, achieving the best average accuracy and Macro-F1 on 18 benchmarks.
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Large-Scale Quantum Kernels for Hyperspectral Data Classification
Simulated fidelity quantum kernels achieve competitive or better accuracy than RBF kernels on Indian Pines binary and multiclass tasks and Methane Detection data without heavy dimensionality reduction.