论文标题
一种基于电源穆德的新型邻居算法
A Novel Nearest Neighbors Algorithm Based on Power Muirhead Mean
论文作者
论文摘要
本文介绍了创新的动力穆赫黑德含义K-Nearest邻居（PMM-KNN）算法，这是一种新型的数据分类方法，将K-Neartiment邻居方法与自适应动力Muirhead Mean Mean Mean Operator相结合。拟议的方法旨在通过利用电源穆赫的均值来解决传统KNN的局限性，以计算每个类中K-Neart邻居的本地手段到查询样本。对不同基准数据集进行的广泛实验证明了PMM-KNN优于其他分类方法。结果表明，各种数据集的准确性在统计学上有显着提高，尤其是具有复杂和高维分布的数据集。功率muirhead的适应性意味着使PMM-KNN有效捕获潜在的数据结构，从而提高了准确性和鲁棒性。这些发现突出了PMM-KNN作为数据分类任务强大而多功能的工具的潜力，鼓励进一步的研究探索其在现实情况下的应用以及Power Muirhead的自动化均值参数，以释放其全部潜力。
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.
