A Novel Nearest Neighbors Algorithm Based on Power Muirhead Mean

TL;DR

This paper presents the Power Muirhead Mean K-Nearest Neighbors (PMM-KNN), a new classification algorithm that improves accuracy by adaptively combining the KNN method with the Power Muirhead Mean operator, especially in complex data scenarios.

Contribution

The paper introduces the PMM-KNN algorithm, integrating the Power Muirhead Mean into KNN to enhance classification performance and robustness on diverse datasets.

Findings

PMM-KNN outperforms traditional KNN and other classifiers in accuracy.

Statistically significant improvements on high-dimensional and complex datasets.

Demonstrates robustness and adaptability of the Power Muirhead Mean in classification tasks.

Abstract

This paper introduces the innovative Power Muirhead Mean K-Nearest Neighbors (PMM-KNN) algorithm, a novel data classification approach that combines the K-Nearest Neighbors method with the adaptive Power Muirhead Mean operator. The proposed methodology aims to address the limitations of traditional KNN by leveraging the Power Muirhead Mean for calculating the local means of K-nearest neighbors in each class to the query sample. Extensive experimentation on diverse benchmark datasets demonstrates the superiority of PMM-KNN over other classification methods. Results indicate statistically significant improvements in accuracy on various datasets, particularly those with complex and high-dimensional distributions. The adaptability of the Power Muirhead Mean empowers PMM-KNN to effectively capture underlying data structures, leading to enhanced accuracy and robustness. The findings highlight the potential of PMM-KNN as a powerful and versatile tool for data classification tasks, encouraging further research to explore its application in real-world scenarios and the automation of Power Muirhead Mean parameters to unleash its full potential.