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A Softened Formulation of Inductive Learning and Its Use for Coronary Disease Data

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Foundations of Intelligent Systems (ISMIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3488))

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Abstract

We present an improved inductive learning method to derive classification rules that correctly describe most of the examples belonging to a class and do not describe most of the examples not belonging to this class. The problem is represented as a modification of the set covering problems solved by a genetic algorithm. Its is employed to medical data on coronary disease, and the results seem to be encouraging.

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Author information

Authors and Affiliations

  1. Systems Research Institute Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk & Grażyna Szkatula

Authors
  1. Janusz Kacprzyk
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  2. Grażyna Szkatula
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Editors and Affiliations

  1. LIRIS - UFR d’Informatique, Université Claude Bernard Lyon 1, 43, boulevard du 11 novembre 1918, 69622, Villeurbanne, France

    Mohand-Said Hacid

  2. Department of Computer Science, State University of New York, 12222, Albany, NY, USA

    Neil V. Murray

  3. Department of Computer Science, University of North Carolina, 28223, Charlotte, NC, USA

    Zbigniew W. Raś

  4. Shimane University, 89-1 Enya-cho Izumo, 6938501, Shimane, Japan

    Shusaku Tsumoto

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© 2005 Springer-Verlag Berlin Heidelberg

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Kacprzyk, J., Szkatula, G. (2005). A Softened Formulation of Inductive Learning and Its Use for Coronary Disease Data. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_21

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  • DOI: https://doi.org/10.1007/11425274_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25878-0

  • Online ISBN: 978-3-540-31949-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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