In the realm of machine learning, the KNN classification algorithm is widely
recognized for its simplicity and efficiency. However, its sensitivity to the K
value poses challenges, especially with small sample sizes or outliers,
impacting classification performance. This article introduces a novel KNN-based
classifier called LMPHNN (Novel Pseudo Nearest Neighbor Classification Method
Using Local Harmonic Mean Distance). LMPHNN leverages harmonic mean distance
(HMD) to improve classification performance based on LMPNN rules and HMD. The
classifier begins by identifying k nearest neighbors for each class and
generates distinct local vectors as prototypes. Pseudo nearest neighbors (PNNs)
are then created based on the local mean for each class, determined by
comparing the HMD of the sample with the initial k group. Classification is
determined by calculating the Euclidean distance between the query sample and
PNNs, based on the local mean of these categories. Extensive experiments on
various real UCI datasets and combined datasets compare LMPHNN with seven
KNN-based classifiers, using precision, recall, accuracy, and F1 as evaluation
metrics. LMPHNN achieves an average precision of 97%, surpassing other methods
by 14%. The average recall improves by 12%, with an average accuracy
enhancement of 5%. Additionally, LMPHNN demonstrates a 13% higher average F1
value compared to other methods. In summary, LMPHNN outperforms other
classifiers, showcasing lower sensitivity with small sample sizes.

LMPHNN 是一种基于 KNN 算法的分类器，通过利用谐波平均距离 (HMD) 来改善分类性能，在小样本量或异常值方面具有较低的敏感性。

一种新的伪最近邻分类方法：使用局部谐波平均距离

A Novel Pseudo Nearest Neighbor Classification Method Using Local  Harmonic Mean Distance

Nearest neighbor (NN) sampling provides more semantic variations than
pre-defined transformations for self-supervised learning (SSL) based image
recognition problems. However, its performance is restricted by the quality of
the support set, which holds positive samples for the contrastive loss. In this
work, we show that the quality of the support set plays a crucial role in any
nearest neighbor based method for SSL. We then provide a refined baseline
(pNNCLR) to the nearest neighbor based SSL approach (NNCLR). To this end, we
introduce pseudo nearest neighbors (pNN) to control the quality of the support
set, wherein, rather than sampling the nearest neighbors, we sample in the
vicinity of hard nearest neighbors by varying the magnitude of the resultant
vector and employing a stochastic sampling strategy to improve the performance.
Additionally, to stabilize the effects of uncertainty in NN-based learning, we
employ a smooth-weight-update approach for training the proposed network.
Evaluation of the proposed method on multiple public image recognition and
medical image recognition datasets shows that it performs up to 8 percent
better than the baseline nearest neighbor method, and is comparable to other
previously proposed SSL methods.

通过引入伪最近邻和平滑权重更新方法，改进了基于最近邻的自监督学习方法，提升了性能。