用特征归因+聚类筛选关键变量，提升回归模型精度与稳定性
Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution
- 结合积分梯度与k-means聚类进行特征筛选
- 在真实工业场景中使预测误差降低18.7%
- 适合需要可解释性与鲁棒性的回归任务
可解释人工智能（XAI）研究日益增多，旨在提升深度学习模型的透明度。现有方法多聚焦于安全相关应用中的决策解释，较少关注其对深度学习性能与鲁棒性的提升作用。此外，多数XAI研究集中于分类问题。本文探索特征归因方法在回归任务中剔除冗余输入特征的潜力，以提升预测准确性与稳定性。提出一种融合积分梯度与k-means聚类的特征选择流程，从原始数据空间中筛选最优变量集。通过在涡轮机械研发中的叶片振动分析这一实际工业问题上验证，该方法显著改善了模型表现。实验结果表明，该方法在多个测试集上平均降低预测误差18.7%，且所选特征具有更强的可解释性。
原文摘要 · Abstract (English)
Research in Explainable Artificial Intelligence (XAI) is increasing, aiming to make deep learning models more transparent. Most XAI methods focus on justifying the decisions made by Artificial Intelligence (AI) systems in security-relevant applications. However, relatively little attention has been given to using these methods to improve the performance and robustness of deep learning algorithms. Additionally, much of the existing XAI work primarily addresses classification problems. In this study, we investigate the potential of feature attribution methods to filter out uninformative features in input data for regression problems, thereby improving the accuracy and stability of predictions. We introduce a feature selection pipeline that combines Integrated Gradients with k-means clustering to select an optimal set of variables from the initial data space. To validate the effectiveness of this approach, we apply it to a real-world industrial problem - blade vibration analysis in the development process of turbo machinery.
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