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Warranty Claim Rate Prediction Using Logged Vehicle Data

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Progress in Artificial Intelligence (EPIA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11804))

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Abstract

Early detection of anomalies, trends and emerging patterns can be exploited to reduce the number and severity of quality problems in vehicles. This is crucially important since having a good understanding of the quality of the product leads to better designs in the future, and better maintenance to solve the current issues. To this end, the integration of large amounts of data that are logged during the vehicle operation can be used to build the model of usage patterns for early prediction. In this study, we have developed a machine learning system for warranty claims by integrating available information sources: Logged Vehicle Data (LVD) and Warranty Claims (WCs). The experimental results obtained from a large data set of heavy duty trucks are used to demonstrate the effectiveness of the proposed system to predict the warranty claims.

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Notes

  1. 1.

    We have used sklearn.feature_selection [5] library (Python) implementations of these feature selection algorithms.

  2. 2.

    We took the advantage of sklearn library in Python to employ this classifier to build the model.

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Author information

Authors and Affiliations

  1. Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden

    Reza Khoshkangini, Sepideh Pashami & Slawomir Nowaczyk

Authors
  1. Reza Khoshkangini
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  2. Sepideh Pashami
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  3. Slawomir Nowaczyk
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Corresponding author

Correspondence to Reza Khoshkangini .

Editor information

Editors and Affiliations

  1. INESC-TEC, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Paulo Moura Oliveira

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. LIACC/UP, University of Porto, Porto, Portugal

    Luís Paulo Reis

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Khoshkangini, R., Pashami, S., Nowaczyk, S. (2019). Warranty Claim Rate Prediction Using Logged Vehicle Data. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_55

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  • DOI: https://doi.org/10.1007/978-3-030-30241-2_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30240-5

  • Online ISBN: 978-3-030-30241-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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