Abstract
This paper presents solutions to the IDA 2016 Industrial Challenge which consists of using machine learning in order to predict whether a specific component of the Air Pressure System of a vehicle faces imminent failure. This problem is modelled as a classification problem, since the goal is to determine if an unobserved instance represents a failure or not. We evaluate various state-of-the-art classification algorithms and investigate how to deal with the imbalanced dataset and with the high amount of missing data. Our experiments showed that the best classifier was cost-wise 92.56 % better than a baseline solution where a random classification is performed.
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Acknowledgements
We acknowledge partial financial support by NSERC Canada, as well as preliminary discussions on this challenge with Philippe Gaudreau.
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Costa, C.F., Nascimento, M.A. (2016). IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures. In: Boström, H., Knobbe, A., Soares, C., Papapetrou, P. (eds) Advances in Intelligent Data Analysis XV. IDA 2016. Lecture Notes in Computer Science(), vol 9897. Springer, Cham. https://doi.org/10.1007/978-3-319-46349-0_33
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DOI: https://doi.org/10.1007/978-3-319-46349-0_33
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