IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures

  • Camila Ferreira CostaEmail author
  • Mario A. Nascimento
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)


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.



We acknowledge partial financial support by NSERC Canada, as well as preliminary discussions on this challenge with Philippe Gaudreau.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

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