A Novel Nearest Neighbors Algorithm Based on Power Muirhead Mean
This is an incremental improvement for data classification tasks, particularly on complex and high-dimensional datasets.
The paper tackled the limitations of traditional K-Nearest Neighbors (KNN) for data classification by introducing the PMM-KNN algorithm, which integrates the Power Muirhead Mean operator to calculate local means, resulting in statistically significant accuracy improvements on diverse benchmark datasets.
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.