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Oddness-Based Classifiers for Boolean or Numerical Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9324))

Abstract

The paper proposes an oddness measure for estimating the extent to which a new item is at odds with a class. Then a simple classification procedure based on the minimization of oddness with respect to the different classes is proposed. Experiments on standard benchmarks with Boolean or numerical data provide good results. The global oddness measure is based on the estimation of the oddness of the new item with respect to the subsets of the classes having a given size.

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Author information

Authors and Affiliations

  1. LARODEC Laboratory, ISG de Tunis, 41 Rue de la Liberté, 2000, Le Bardo, Tunisia

    Myriam Bounhas

  2. Emirates College of Technology, P.O. Box: 41009, Abu Dhabi, United Arab Emirates

    Myriam Bounhas

  3. IRIT – CNRS, 118, Route de Narbonne, Toulouse, France

    Henri Prade & Gilles Richard

  4. QCIS, University of Technology, Sydney, Australia

    Henri Prade & Gilles Richard

Authors
  1. Myriam Bounhas
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  2. Henri Prade
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  3. Gilles Richard
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Corresponding author

Correspondence to Myriam Bounhas .

Editor information

Editors and Affiliations

  1. Technische Universität Dresden, Dresden, Germany

    Steffen Hölldobler

  2. Technische Universität Dresden, Dresden, Germany

  3. Free University Bozen-Bolzano, Bozen-Bolzano, Italy

    Rafael Peñaloza

  4. Technische Universität Dresden, Dresden, Germany

    Sebastian Rudolph

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© 2015 Springer International Publishing Switzerland

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Bounhas, M., Prade, H., Richard, G. (2015). Oddness-Based Classifiers for Boolean or Numerical Data. In: Hölldobler, S., , Peñaloza, R., Rudolph, S. (eds) KI 2015: Advances in Artificial Intelligence. KI 2015. Lecture Notes in Computer Science(), vol 9324. Springer, Cham. https://doi.org/10.1007/978-3-319-24489-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-24489-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24488-4

  • Online ISBN: 978-3-319-24489-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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