This paper presents a new solution for choosing the K parameter in the
k-nearest neighbor (KNN) algorithm, the solution depending on the idea of
ensemble learning, in which a weak KNN classifier is used each time with a
different K, starting from one to the square root of the size of the training
set. The results of the weak classifiers are combined using the weighted sum
rule. The proposed solution was tested and compared to other solutions using a
group of experiments in real life problems. The experimental results show that
the proposed classifier outperforms the traditional KNN classifier that uses a
different number of neighbors, is competitive with other classifiers, and is a
promising classifier with strong potential for a wide range of applications.

该研究提出了一种基于集成学习的解决方案来选择 k 最近邻算法中的 K 参数，该解决方案从每次使用不同的 K 值的弱 KNN 分类器开始，最终使用加权求和规则将弱分类器的结果进行组合，实验结果表明该分类器优于传统 KNN 分类器且具有广泛的应用潜力。