Title: Shapley-Based Data Valuation with Mutual Information: A Key to Modified K-Nearest Neighbors Show Chinese title

Title: 基于Shapley值的数据价值评估：互信息在改进的K-最近邻中的关键作用

Abstract: The K-Nearest Neighbors (KNN) algorithm is widely used for classification and regression; however, it suffers from limitations, including the equal treatment of all samples. We propose Information-Modified KNN (IM-KNN), a novel approach that leverages Mutual Information ($I$) and Shapley values to assign weighted values to neighbors, thereby bridging the gap in treating all samples with the same value and weight. On average, IM-KNN improves the accuracy, precision, and recall of traditional KNN by 16.80%, 17.08%, and 16.98%, respectively, across 12 benchmark datasets. Experiments on four large-scale datasets further highlight IM-KNN's robustness to noise, imbalanced data, and skewed distributions. Show Chinese abstract

Abstract: K-最近邻（KNN）算法广泛用于分类和回归；然而，它存在一些限制，包括对所有样本一视同仁。 我们提出了信息修改的KNN（IM-KNN），这是一种新方法，利用互信息（$I$）和Shapley值为邻居分配加权值，从而弥补了对所有样本赋予相同价值和权重的不足。 在12个基准数据集上，IM-KNN平均将传统KNN的准确性、精确度和召回率分别提高了16.80%、17.08%和16.98%。 在四个大规模数据集上的实验进一步突显了IM-KNN对噪声、不平衡数据和偏态分布的鲁棒性。