DW-KNN通过双重加权提升分类可靠性，让模型更可信、更稳定。
DW-KNN: A Transparent Local Classifier Integrating Distance Consistency and Neighbor Reliability
- 融合距离一致性和邻居可信度，动态调整邻居权重
- 平均准确率0.8988，误差波动仅0.0156，优于多数基线方法
- 适合需要可解释性的高风险场景，如医疗与金融决策
K-近邻（KNN）是广泛使用的机器学习分类器。然而，标准距离加权KNN及其变体假设所有k个邻居均同样可靠。在异质特征空间中，这一假设限制了预测的可靠性。本文提出双加权KNN（DW-KNN），通过引入指数距离与邻居有效性双重加权机制，实现实例级可解释性，抑制噪声或误标样本，并降低超参数敏感性。在9个数据集上的综合评估显示，DW-KNN平均准确率达0.8988，在六种方法中排名第二，仅比表现最佳的集成KNN低0.2%。其交叉验证方差最低，仅为0.0156，表明预测稳定性强。统计显著性检验（p < 0.001）证实，相比紧凑性加权KNN提升4.09%，相比核加权KNN提升1.13%。该方法为复杂自适应方案提供了简单而有效的替代方案，尤其适用于需可解释预测的高风险应用。
原文摘要 · Abstract (English)
K-Nearest Neighbors (KNN) is one of the most used ML classifiers. However, if we observe closely, standard distance-weighted KNN and relative variants assume all 'k' neighbors are equally reliable. In heterogeneous feature space, this becomes a limitation that hinders reliability in predicting true levels of the observation. We propose DW-KNN (Double Weighted KNN), a transparent and robust variant that integrates exponential distance with neighbor validity. This enables instance-level interpretability, suppresses noisy or mislabeled samples, and reduces hyperparameter sensitivity. Comprehensive evaluation on 9 data-sets helps to demonstrate that DW-KNN achieves 0.8988 accuracy on average. It ranks 2nd among six methods and within 0.2% of the best-performing Ensemble KNN. It also exhibits the lowest cross-validation variance (0.0156), indicating reliable prediction stability. Statistical significance test confirmed ($p < 0.001$) improvement over compactness weighted KNN (+4.09\%) and Kernel weighted KNN (+1.13\%). The method provides a simple yet effective alternative to complex adaptive schemes, particularly valuable for high-stakes applications requiring explainable predictions.
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