The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.

翻译：自动化数据采集方案的普及和传感器技术的进步，使我们能够实时监测的数据量不断增加。然而，由于标注成本高昂且质量检测需要时间，数据通常以无标注形式存在。这促使主动学习被广泛应用于开发软传感器和预测模型。在生产过程中，无需通过随机抽样获取产品信息，而是通过评估未标注数据的信息含量来获取标签。文献中已提出多种回归查询策略框架，但多数研究集中于静态池场景。本文针对数据流场景提出一种新策略：实例按顺序提供给学习器，学习器需立即决定是否进行质量检测以获取标签或丢弃该实例。该策略受最优实验设计理论启发，通过为未标注数据点的信息量设置阈值来解决决策过程的迭代问题。通过数值模拟和Tennessee Eastman过程模拟器对所提方法进行评估，结果表明，选择算法建议的样本能够更快降低预测误差。