Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913511943176192 |
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| author | Metta, Carlo Gregnanin, Marco Papini, Andrea Galfrè, Silvia Giulia Fois, Andrea Morandin, Francesco Fantozzi, Marco Parton, Maurizio |
| author_facet | Metta, Carlo Gregnanin, Marco Papini, Andrea Galfrè, Silvia Giulia Fois, Andrea Morandin, Francesco Fantozzi, Marco Parton, Maurizio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13877 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024 Metta, Carlo Gregnanin, Marco Papini, Andrea Galfrè, Silvia Giulia Fois, Andrea Morandin, Francesco Fantozzi, Marco Parton, Maurizio Machine Learning 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. |
| title | Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024 |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.13877 |