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Rapid Anomaly Detection Using Integrated Prudence Analysis (IPA)

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11154))

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Abstract

Integrated Prudence Analysis has been proposed as a method to maximize the accuracy of rule based systems. The paper presents evaluation results of the three Prudence methods on public datasets which demonstrate that combining attribute-based and structural Prudence produces a net improvement in Prudence Accuracy.

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References

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Author information

Authors and Affiliations

  1. PwC, Melbourne, Australia

    Omaru Maruatona

  2. Federation University, Ballarat, Australia

    Peter Vamplew & Richard Dazeley

  3. La Trobe University, Melbourne, Australia

    Paul A. Watters

Authors
  1. Omaru Maruatona
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  2. Peter Vamplew
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  3. Richard Dazeley
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  4. Paul A. Watters
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Corresponding author

Correspondence to Omaru Maruatona .

Editor information

Editors and Affiliations

  1. University of Melbourne, Melbourne, VIC, Australia

    Mohadeseh Ganji

  2. University of Melbourne, Melbourne, VIC, Australia

    Lida Rashidi

  3. McGill University, Montreal, QC, Canada

    Benjamin C. M. Fung

  4. Griffith University, Gold Coast, QLD, Australia

    Can Wang

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Maruatona, O., Vamplew, P., Dazeley, R., Watters, P.A. (2018). Rapid Anomaly Detection Using Integrated Prudence Analysis (IPA). In: Ganji, M., Rashidi, L., Fung, B., Wang, C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-04503-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04502-9

  • Online ISBN: 978-3-030-04503-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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