Corpus ID: 236177826

Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies

  title={Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption Anomalies},
  author={A. Barbado and 'Oscar Corcho},
Identifying anomalies in the fuel consumption of the vehicles of a fleet is a crucial aspect for optimizing consumption and reduce costs. However, this information alone is insufficient, since fleet operators need to know the causes behind anomalous fuel consumption. We combine unsupervised anomaly detection techniques, domain knowledge and interpretable Machine Learning models for explaining potential causes of abnormal fuel consumption in terms of feature relevance. The explanations are used… Expand


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  • Ana Antoniette C. Illahi, A. Bandala, E. Dadios
  • Computer Science
  • 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )
  • 2019
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Interpretable machine learning has become a popular research direction as deep neural networks (DNNs) have become more powerful and their applications more mainstream, yet DNNs remain difficult toExpand