Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024
<|reference_start|>Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024: 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.<|reference_end|>
@article{metta2024achieving,
    title={Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for
  Volvo's Discovery Challenge at ECML-PKDD 2024},
    author={Carlo Metta, Marco Gregnanin, Andrea Papini, Silvia Giulia Galfr`e,
  Andrea Fois, Francesco Morandin, Marco Fantozzi, Maurizio Parton},
    journal={arXiv preprint arXiv:2409.13877},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13877},
    primaryClass={cs.LG}
}