用核密度估计优化模型集成预测，提升准确率与可信度。
Evaluation of Bagging Predictors with Kernel Density Estimation and Bagging Score
- 用核密度估计找最优集成预测值，替代平均或中位数
- 新方法在多个数据集上误差更低，无需调参或特征选择
- 提供置信度评分，适合需要可靠性评估的场景
对于一组由不同训练得到的机器学习模型预测结果（即集成预测），传统做法是取均值。然而在某些参数区域，该方法可能偏离真实值。本文提出一种基于核密度估计（KDE）的方法，在神经网络非线性回归中确定代表性预测值 y_BS，同时生成关联的质量指标 beta_BS（称为袋装得分，BS），反映集成预测的置信度。实验表明，该方法优于传统的均值或中位数策略。此外，与文献中多种非线性回归方法对比，该方法在各项误差指标上均排名第一，且未使用任何优化或特征选择技术。
原文摘要 · Abstract (English)
For a larger set of predictions of several differently trained machine learning models, known as bagging predictors, the mean of all predictions is taken by default. Nevertheless, this proceeding can deviate from the actual ground truth in certain parameter regions. An approach is presented to determine a representative y_BS from such a set of predictions using Kernel Density Estimation (KDE) in nonlinear regression with Neural Networks (NN) which simultaneously provides an associated quality criterion beta_BS, called Bagging Score (BS), that reflects the confidence of the obtained ensemble prediction. It is shown that working with the new approach better predictions can be made than working with the common use of mean or median. In addition to this, the used method is contrasted to several approaches of nonlinear regression from the literatur, resulting in a top ranking in each of the calculated error values without using any optimization or feature selection technique.
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