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Time Prediction of the Next Refueling Event: A Case Study

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Book cover Advances in Artificial Intelligence (Canadian AI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10233))

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Abstract

A case study of finding the best algorithm for predicting the time of the next refueling event from an incomplete, crowd-sourced data set is presented. We considered ten algorithms including nine experts plus one ensemble (learner) method that performs machine learning using the other nine experts. An experiment on one dimensional crowd-sourced data showed that prediction with the ensemble method is more accurate than prediction with any of the individual experts.

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Author information

Authors and Affiliations

  1. University of Regina, Regina, Saskatchewan, Canada

    S. Mohammad Mirbagheri & Howard J. Hamilton

Authors
  1. S. Mohammad Mirbagheri
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  2. Howard J. Hamilton
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Corresponding author

Correspondence to S. Mohammad Mirbagheri .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, Saskatchewan, Canada

    Malek Mouhoub

  2. University of Montreal, Montreal, Québec, Canada

    Philippe Langlais

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Mirbagheri, S.M., Hamilton, H.J. (2017). Time Prediction of the Next Refueling Event: A Case Study. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-57351-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57350-2

  • Online ISBN: 978-3-319-57351-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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