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

TL;DR

This paper introduces LMPHNN, a new KNN-based classifier that uses local harmonic mean distance to improve classification accuracy, especially with small samples and outliers, outperforming existing methods on various datasets.

Contribution

The paper presents LMPHNN, a novel KNN classifier utilizing harmonic mean distance and pseudo nearest neighbors to enhance performance and robustness over traditional KNN algorithms.

Findings

LMPHNN achieves an average precision of 97%, outperforming other classifiers by 14%.

LMPHNN improves recall by 12% and accuracy by 5% on average.

LMPHNN demonstrates 13% higher F1 score compared to existing methods.

Abstract

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.