Online sensing and computational resources in Industrial Cyber-physical
Systems (ICPS) facilitate AI-driven decision-making. Yet, issues with data
quality, such as imbalanced classes, hinder AI models trained offline. To
address this, AI models are updated online with streaming data for continuous
improvement. Supervised learning models, however, face challenges in selecting
quality streaming samples for updates due to annotation constraints. Active
learning methods in literature offer solutions by focusing on under-represented
or well-represented regions. Balancing these strategies in changing
manufacturing contexts is challenging. Some acquisition criteria learned by AI
dynamically adapt but may not consistently handle frequent changes. We
introduce an ensemble active learning method, CBEAL, employing active learning
agents specifically for exploration or exploitation. Weights of agents are
adjusted based on agent decision effectiveness. CBEAL optimally guides data
acquisition, minimizing human annotation. Our theoretical analysis and
empirical studies validate CBEAL's efficiency in ICPS manufacturing process
modeling.

在线感知和计算资源在工业物联网系统（Industrial Cyber-physical Systems，ICPS）中促进了以人工智能为驱动的决策制定。然而，数据质量的问题，如不平衡类别，阻碍了离线训练的人工智能模型。为了解决这个问题，人工智能模型通过流式数据进行在线更新，持续改进。然而，监督学习模型在选择质量流式样本进行更新时面临注释限制的挑战。文献中的主动学习方法通过关注不充分或充分代表的区域提供了解决方案。在不断变化的制造背景下平衡这些策略是具有挑战性的。一些人工智能学到的获取准则动态地适应，但可能不一致地处理频繁变化。我们引入了一种集成主动学习方法 CBEAL，专门用于探索或开发的主动学习代理。代理的权重根据代理决策的有效性进行调整。CBEAL 在最小化人工注释的同时，优化地指导数据获取。我们的理论分析和实证研究验证了 CBEAL 在 ICPS 制造过程建模方面的效率。

制造业中基于情境赌博机的集合主动学习

Ensemble Active Learning by Contextual Bandits for AI Incubation in  Manufacturing

The realization that AI-driven decision-making is indispensable in todays
fast-paced and ultra-competitive marketplace has raised interest in industrial
machine learning (ML) applications significantly. The current demand for
analytics experts vastly exceeds the supply. One solution to this problem is to
increase the user-friendliness of ML frameworks to make them more accessible
for the non-expert. Automated machine learning (AutoML) is an attempt to solve
the problem of expertise by providing fully automated off-the-shelf solutions
for model choice and hyperparameter tuning. This paper analyzed the potential
of AutoML for applications within business analytics, which could help to
increase the adoption rate of ML across all industries. The H2O AutoML
framework was benchmarked against a manually tuned stacked ML model on three
real-world datasets to test its performance, robustness, and reliability. The
manually tuned ML model could reach a performance advantage in all three case
studies used in the experiment. Nevertheless, the H2O AutoML package proved to
be quite potent. It is fast, easy to use, and delivers reliable results, which
come close to a professionally tuned ML model. The H2O AutoML framework in its
current capacity is a valuable tool to support fast prototyping with the
potential to shorten development and deployment cycles. It can also bridge the
existing gap between supply and demand for ML experts and is a big step towards
fully automated decisions in business analytics.

本文分析了自动机器学习在企业分析应用中的潜力，并将 H2O AutoML 框架与手工调整的 ML 模型在三个真实数据集上进行了基准测试，发现 H2O AutoML 框架快速、易用、可靠，表现接近手工调优的 ML 模型，是一个有价值的工具，能够支持快速原型设计，缩短开发和部署周期，并能够弥合 ML 专家供需之间的差距。