In this research paper, we introduce a novel classification method aimed at improving the performance of the K-Nearest Neighbors (KNN) algorithm. Our approach leverages Mutual Information (MI) to enhance the significance of weights and draw inspiration from Shapley values, a concept originating from cooperative game theory, to refine value allocation. The fundamental concept underlying KNN is the classification of samples based on the majority thorough their k-nearest neighbors. While both the distances and labels of these neighbors are crucial, traditional KNN assigns equal weight to all samples and prevance considering the varying importance of each neighbor based on their distances and labels. In the proposed method, known as Information-Modified KNN (IMKNN), we address this issue by introducing a straightforward algorithm. To evaluate the effectiveness of our approach, it is compared with 7 contemporary variants of KNN, as well as the traditional KNN. Each of these variants exhibits its unique advantages and limitations. We conduct experiments on 12 widely-used datasets, assessing the methods' performance in terms of accuracy, precision and recall. Our study demonstrates that IMKNN consistently outperforms other methods across different datasets and criteria by highlighting its superior performance in various classification tasks. These findings underscore the potential of IMKNN as a valuable tool for enhancing the capabilities of the KNN algorithm in diverse applications.

翻译：本研究提出一种新型分类方法，旨在提升K近邻算法的性能。该方法利用互信息增强权重显著性，并借鉴合作博弈论中的夏普利值概念优化价值分配。K近邻的基本原理是基于样本的k个最近邻通过多数表决进行分类。尽管这些近邻的距离和标签均至关重要，传统KNN却对所有样本赋予相同权重，未能考虑各邻域因距离和标签差异而产生的不同重要性。所提出的信息修正KNN方法通过引入简洁算法解决了该问题。为评估方法有效性，我们将其与当前7种KNN变体及传统KNN进行对比——每种变体均具有独特优势与局限。基于12个广泛采用的数据集，我们以准确率、精确率和召回率三项指标评估各方法性能。研究表明，IMKNN在不同数据集与评价标准下均持续优于其他方法，充分彰显其在多样化分类任务中的卓越表现。这些发现凸显了IMKNN作为增强KNN算法能力的工具在各类应用中的重要潜力。