Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024

Carlo Metta, Marco Gregnanin, Andrea Papini, Silvia Giulia Galfrè, Andrea Fois +3 more
9/20/2024
cs.LG

No official code found. Best implementation: CuriosAI/Volvo_Discovery_Challenge_ECML_PKDD_2024 (3 stars, Apache-2.0, last commit 2024-08-28).

Abstract

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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Code Implementations(1)

CuriosAI/Volvo_Discovery_Challenge_ECML_PKDD_2024Community100%
Apache-2.0

Implementation of the 2nd leaderboard submisison to the 2024 ECML PKDD Discovery Challenge

31PythonAug 28, 20242 years agoApache-2.0

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Cite this paper

@article{metta2024achieving,
  title  = {Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024},
  author = {Carlo Metta and Marco Gregnanin and Andrea Papini and Silvia Giulia Galfrè and Andrea Fois and Francesco Morandin and Marco Fantozzi and Maurizio Parton},
  year   = {2024},
  eprint = {2409.13877},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.13877v1}
}

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