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An ensemble active learning for a fluidized bed granulation in the pharmaceutical industry
Journal of Process Control ( IF 4.2 ) Pub Date : 2022-08-29 , DOI: 10.1016/j.jprocont.2022.08.007
Zhongxin Chen, Yongwei Tang, Zenglin Gao, Jun Zhou, Panling Huang

Active learning (AL), allowing to query the labels by an oracle, can dwindle the generalization error from a few labeled samples, thus well-motivating in the scenario where there are fewer labeled samples and abundant unlabeled samples. However, the query samples cannot be exploited efficiently in the existing methods based on AL for regression tasks, and AL is typically applied for classification problems. In this paper, an active learning framework (CALF) is proposed to improve the efficiency with fewer query samples, aiming at the moisture content prediction in fluidized bed granulation. The proposed method, based on conditional variational auto-encoder (CVAE) and selective ensemble algorithm, can efficiently incorporate the information in query samples into the labeled sample space. The game of CVAE and selective ensemble algorithm improves the performance of the framework effectively, and its effectiveness is verified by the results obtained from the large batch of fluidized bed granulating experiments.



中文翻译:

制药行业流化床制粒的集成主动学习,制药行业流化床制粒的集成主动学习

主动学习(AL)允许通过预言机查询标签,可以减少少数标记样本的泛化误差,从而在标记样本较少且未标记样本丰富的情况下具有良好的激励作用。然而,现有的基于 AL 的回归任务方法无法有效地利用查询样本,而 AL 通常应用于分类问题。在本文中,针对流化床制粒中的水分含量预测,提出了一种主动学习框架(CALF),以通过更少的查询样本来提高效率。该方法基于条件变分自动编码器(CVAE)和选择性集成算法,可以有效地将查询样本中的信息整合到标记样本空间中。

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主动学习(AL)允许通过预言机查询标签,可以减少少数标记样本的泛化误差,从而在标记样本较少且未标记样本丰富的情况下具有良好的激励作用。然而,现有的基于 AL 的回归任务方法无法有效地利用查询样本,而 AL 通常应用于分类问题。在本文中,针对流化床制粒中的水分含量预测,提出了一种主动学习框架(CALF),以通过更少的查询样本来提高效率。该方法基于条件变分自动编码器(CVAE)和选择性集成算法,可以有效地将查询样本中的信息整合到标记样本空间中。

更新日期:2022-08-29
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