Machine learningMachine learning
可解释 K-近邻算法
可解释 K-近邻算法 (XKNN) 通过结构化的事后解释或内置解释机制来增强经典的 KNN 分类器或回归器，揭示哪些检索到的邻居、哪些特征以及哪些距离贡献驱动了每一次个体预测——使模型的推理过程透明且可供人类决策者审计。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI: 10.1109/TIT.1967.1053964 ↗
- Papernot, N. & McDaniel, P. (2018). Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning. arXiv preprint arXiv:1803.04765. link ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable K-Nearest Neighbors (XKNN). ScholarGate. https://scholargate.app/zh/machine-learning/explainable-k-nearest-neighbors
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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