用LSTM和伪标签预测沃尔沃卡车部件维修需求，效果显著。
Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024
- 用LSTM模型迭代标记测试数据，模拟真实预测场景。
- 在测试集上达到0.879的宏平均F1分数，表现稳健。
- 适合工业界做预测性维护的机器学习落地参考。
本文介绍了在ECML-PKDD 2024沃尔沃发现挑战赛中获得第二名的方法。针对沃尔沃卡车某部件的维护需求预测任务，我们采用长短期记忆网络（LSTM）与伪标签技术。通过将训练数据处理为与测试集结构一致，并使用基础LSTM模型对测试数据进行迭代标注，逐步提升模型预测能力。该方法最终实现宏平均F1分数0.879，在预测性维护任务中展现出优异性能。本工作为机器学习技术在工业场景中的有效应用提供了实用参考。
原文摘要 · Abstract (English)
This paper presents the second-place methodology in the Volvo Discovery Challenge at ECML-PKDD 2024, where we used Long Short-Term Memory networks and pseudo-labeling to predict maintenance needs for a component of Volvo trucks. We processed the training data to mirror the test set structure and applied a base LSTM model to label the test data iteratively. This approach refined our model's predictive capabilities and culminated in a macro-average F1-score of 0.879, demonstrating robust performance in predictive maintenance. This work provides valuable insights for applying machine learning techniques effectively in industrial settings.
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