发表机构
Michigan Technological University（密歇根理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对熔融沉积建模过程数据集，围绕强化学习启发的策略更新机制，利用SHAP XAI生成特征子集，训练多个机器学习模型，探索最优模型-特征配置，提升了预测准确性和稳定性，改进了测试集AUC等指标。
AI 中文摘要
本研究致力于开发一个自动化数据处理（ADP）框架，旨在评估和强化熔融沉积建模（FDM）过程数据集中预测任务的最优机器学习模型-特征组合。该方法围绕强化学习启发的策略更新机制展开，在217个数据集上，多个机器学习模型在完整特征集和通过基于Shapley的可解释人工智能（SHAP XAI）选择的特征子集上进行训练。每一轮，框架评估每个模型-特征对的预测准确性和F1分数，计算标量奖励并更新Q值以指导未来模型选择。利用SHAP XAI特征重要性生成精简但信息丰富的特征子集，使框架能够探索维度性能。结果表明，通过XAI算法利用ADP框架成功收敛到具有更高准确性和稳定性的最优模型-特征配置。具体而言，与基线完整特征配置相比，所提出的框架将测试集AUC从0.9248提高到0.9731，并将平均奖励值提高了五十多个百分点。
英文摘要
This study contributes toward development of an Automated Data Processing (ADP) framework designed to evaluate and reinforce optimal machine learning model-feature combinations for predictive tasks in fused deposition modeling (FDM) process datasets. The methodology is centered around a reinforcement learning-inspired policy updating mechanism, where multiple machine learning models are trained on both full feature sets and feature subsets selected through Shapley-based Explainable AI (SHAP XAI) across 217 datasets. At each episode, the framework assesses the predictive accuracy and F1-scores of each model-feature pair, computes a scalar reward, and updates $Q$ values to guide future model selection. SHAP XAI feature importance was employed to generate reduced yet informative feature subsets to enable the framework to explore performance with dimensionality. The policy was shown to evolve over multiple episodes, with reward distributions used to visualize performance stability. Overall, results indicate that leveraging the ADP framework through XAI algorithms successfully converges toward optimal model-feature configurations with improved accuracy and stability. Specifically, the proposed framework improves the test-set AUC from 0.9248 to 0.9731 and increases the mean reward value by more than fifty percent compared with the baseline full-feature configuration.
