QGB-W$k$NN: Quantum Granular-Ball Learning for Robust Classification
Nearest-neighbor classification is widely used in machine learning, yet existing methods often suffer from low computational efficiency and limited robustness in noisy environments. To jointly address these challenges, this paper proposes an efficient and reliable weighted $K$-nearest neighbor classification framework based on quantum granular balls, termed QGB-W$k$NN. The proposed framework improves computational efficiency by integrating quantum-enhanced granular-ball representation with hierarchical nearest-neighbor search, while enhancing classification reliability through a purity-aware weighted decision mechanism. Specifically, quantum-kernel granular balls are constructed to reduce...
| Authors | Suzhen Yuan, Dehang Chen, Lifeng Shen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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