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Using Statistical Models and Case-Based Reasoning in Claims Prediction: Experience from a Real-World Problem

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Applications and Innovations in Expert Systems VI

Abstract

Case-Based Reasoning (CBR) has been widely used in many real-world applications. In general, CBR systems propose their answers based on solutions attached with the most similar cases retrieved from their case bases. However, in our vehicle insurance domain where the dataset contains large amount of inconsistencies, proposing solutions based only on the most similar cases result in unacceptable answers. In this paper, we propose a hybrid reasoning algorithm which employs a number of statistical models derived from analysis of the entire dataset as alternative reasoning method. Result of our experiments have shown that the use of these models enable our experimental system to propose better solutions than answers proposed based only on the closest matched cases.

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Author information

Authors and Affiliations

  1. School of Mathematical and Computer Science, The University of New England, Armidale, NSW, 2351, Australia

    Jirapun Daengdej, Dickson Lukose & Robert Murison

Authors
  1. Jirapun Daengdej
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  2. Dickson Lukose
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  3. Robert Murison
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Editor information

Editors and Affiliations

  1. Intelligent Applications Ltd., 1 Michaelson Square, Livingston, W. Lothian, Scotland

    Robert W. Milne BSc, PhD

  2. Artificial Intelligence Applications Institute, University of Edinburgh, Edinburgh, Scotland

    Ann L. Macintosh BSc, CEng

  3. Faculty of Technology, University of Portsmouth, Portsmouth, UK

    Max Bramer BSc, PhD, CEng

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© 1999 Springer-Verlag London

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Daengdej, J., Lukose, D., Murison, R. (1999). Using Statistical Models and Case-Based Reasoning in Claims Prediction: Experience from a Real-World Problem. In: Milne, R.W., Macintosh, A.L., Bramer, M. (eds) Applications and Innovations in Expert Systems VI. Springer, London. https://doi.org/10.1007/978-1-4471-0575-6_16

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  • DOI: https://doi.org/10.1007/978-1-4471-0575-6_16

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-087-3

  • Online ISBN: 978-1-4471-0575-6

  • eBook Packages: Springer Book Archive

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