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Fusion of Rule-Based and Sample-Based Classifiers – Probabilistic Approach

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Computer and Information Sciences - ISCIS 2005 (ISCIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3733))

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Abstract

The present paper is devoted to the pattern recognition methods for combining heterogeneous sets of learning data: set of training examples and the set of expert rules with unprecisely formulated weights understood as conditional probabilities. Adopting the probabilistic model two concepts of recognition learning are proposed. In the first approach two classifiers trained on homogeneous data set are generated and next their decisions are combined using local weighted voting combination rule. In the second method however, one set of data is transformed into the second one and next only one classifier trained on homogeneous set of data is used. Presented algorithms were practically applied to the computer-aided diagnosis of acute renal failure in children and results of their classification accuracy are given.

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Author information

Authors and Affiliations

  1. Faculty of Electronics, Chair of Systems and Computer Networks, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzynski

Authors
  1. Marek Kurzynski
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Boğaziçi University, 34342, Bebek, Istanbul, Turkey

    pInar Yolum  & Can Özturan  & 

  2. Computer Engineering Department, Boğaziçi University, 34342, Bebek, İstanbul, Turkey

    Tunga Güngör

  3. Computer Engineering Department, Bogazici University, 80815, Bebek, Istanbul, Turkey

    Fikret Gürgen

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Kurzynski, M. (2005). Fusion of Rule-Based and Sample-Based Classifiers – Probabilistic Approach. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds) Computer and Information Sciences - ISCIS 2005. ISCIS 2005. Lecture Notes in Computer Science, vol 3733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569596_56

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  • DOI: https://doi.org/10.1007/11569596_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29414-6

  • Online ISBN: 978-3-540-32085-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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