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.

翻译：可解释人工智能（XAI）领域的研究日益增多，其目标是使深度学习模型更加透明。大多数XAI方法侧重于在安全相关应用中为人工智能（AI）系统的决策提供依据。然而，利用这些方法来提升深度学习算法的性能与鲁棒性尚未得到充分关注。此外，现有XAI研究主要针对分类问题。本研究探讨了特征归因方法在回归问题中过滤输入数据非信息特征方面的潜力，从而提升预测的准确性与稳定性。我们提出了一种特征选择流程，将积分梯度与k-means聚类相结合，从初始数据空间中选择最优变量集合。为验证该方法的有效性，我们将其应用于涡轮机械研发过程中的叶片振动分析这一实际工业问题。