Data-driven KNN modeling via optimized weighted distance learning

doi:10.1016/j.asoc.2026.115811

2026-06-24

PRE

Liangliang Zhuang

Gauthier Barbosa *

Zhisheng Ye

DOI：10.1016/j.asoc.2026.115811

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摘要

En 中文

• 基于数据驱动的KNN框架，具有特征级和邻居级权重的自适应距离学习。• 线性化近似得到用于自适应特征权重估计的可解线性规划。• 理论分析建立了关键距离属性和Fisher一致性。• 实验表明，在模拟和真实数据集上均表现出竞争性和稳定性。

期刊

Applied Soft Computing

IF:

6.6

论文数:

1.4W

被引数:

4.8W

机构

nanjing university of science and technology

学者数:

4.1K

论文数: 1.4K

被引数: 0

National University of Singapore

学者数:

7.6W

论文数: 6.5W

被引数: 11.4W

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