利用深度生成数据增强提升过时预测：面向低数据工业应用的半监督框架
Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications
AI总结：
本文提出一种基于深度生成数据增强的半监督框架，通过生成新过时案例扩充训练数据，并适配经典监督分类器进行半监督学习，在低数据工业场景下实现了最先进的电子元器件过时预测性能。
摘要：
电子元器件过时问题在长生命周期系统中尤为关键。为减轻其影响，人们采用了多种过时管理方法，其中过时预测是一种备受关注且突出的方法。因此，已提出了大量基于机器学习的预测方法。然而，机器学习模型需要大量相关数据才能达到高精度，而在某些情况下当前的过时态势中缺乏此类数据。本文提出了一种基于深度学习的新型过时预测框架。该框架通过深度生成建模解决可用数据不足的问题，生成新的过时案例并用于扩充训练数据集。扩充后的数据集随后用于训练基于经典机器学习的过时预测模型。为了利用扩充数据集训练经典预测模型，现有的经典监督学习分类器在该框架内被适配用于半监督学习。所提出的框架在基准数据集上展示了最先进的结果。
英文摘要：
The challenge of electronic component obsolescence is particularly critical in systems with long life cycles. Various obsolescence management methods are employed to mitigate its impact, with obsolescence forecasting being a highly sought-after and prominent approach. As a result, numerous machine learning-based forecasting methods have been proposed. However, machine learning models require a substantial amount of relevant data to achieve high precision, which is lacking in the current obsolescence landscape in some situations. This work introduces a novel framework for obsolescence forecasting based on deep learning. The proposed framework solves the lack of available data through deep generative modeling, where new obsolescence cases are generated and used to augment the training dataset. The augmented dataset is then used to train a classical machine learning-based obsolescence forecasting model. To train classical forecasting models using augmented datasets, existing classical supervised-learning classifiers are adapted for semi-supervised learning within this framework. The proposed framework demonstrates state-of-the-art results on benchmarking datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
