Workpiece quality prediction is very important in modern manufacturing industry. However, traditional machine learning methods are very sensitive to their hyperparameters, making the tuning of the machine learning methods essential to improve the prediction performance. Hyperparameter optimization (HPO) approaches are applied attempting to tune hyperparameters, such as grid search and random search. However, the hyperparameters space for workpiece quality prediction model is high dimension and it consists with continuous, combinational and conditional types of hyperparameters, which is difficult to be tuned. In this article, a new automatic machine learning based HPO, named adaptive Tree Pazen Estimator (ATPE), is proposed for workpiece quality prediction in high dimension. In the proposed method, it can iteratively search the best combination of hyperparameters in the automatic way. During the warm-up process for ATPE, it can adaptively adjust the hyperparameter interval to guide the search. The proposed ATPE is tested on sparse stack autoencoder based MNIST and XGBoost based WorkpieceQuality dataset, and the results show that ATPE provides the state-of-the-art performances in high-dimensional space and can search the hyperparameters in reasonable range by comparing with Tree Pazen Estimator, annealing, and random search, showing its potential in the field of workpiece quality prediction.

中文翻译：

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一种新的基于自动机器学习的工件质量预测超参数优化

工件质量预测在现代制造业中非常重要。然而，传统的机器学习方法对其超参数非常敏感，这使得机器学习方法的调整对于提高预测性能至关重要。超参数优化 (HPO) 方法用于尝试调整超参数，例如网格搜索和随机搜索。然而，工件质量预测模型的超参数空间是高维的，由连续、组合和条件类型的超参数组成，难以调整。在本文中，提出了一种新的基于自动机器学习的 HPO，称为自适应 Tree Pazen Estimator (ATPE)，用于高维工件质量预测。在提出的方法中，它可以以自动方式迭代搜索超参数的最佳组合。在 ATPE 的预热过程中，它可以自适应地调整超参数区间来指导搜索。提出的 ATPE 在基于稀疏堆栈自动编码器的 MNIST 和基于 XGBoost 的 WorkpieceQuality 数据集上进行了测试，结果表明 ATPE 在高维空间中提供了最先进的性能，并且可以通过与 Tree 比较来搜索合理范围内的超参数Pazen Estimator、退火和随机搜索，显示其在工件质量预测领域的潜力。