Inductive Learning for Case-Based Diagnosis with Multiple Faults

  • Joachim Baumeister
  • Martin Atzmüller
  • Frank Puppe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2416)


We present adapted inductive methods for learning similarities, parameter weights and diagnostic profiles for case-based reasoning. All of these methods can be refined incrementally by applying different types of background knowledge. Diagnostic profiles are used for extending the conventional CBR to solve cases with multiple faults. The context of our work is to supplement a medical documentation and consultation system by CBR techniques, and we present an evaluation with a real-world case base.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Joachim Baumeister
    • 1
  • Martin Atzmüller
    • 1
  • Frank Puppe
    • 1
  1. 1.Department of Computer ScienceUniversity of WürzburgWürzburgGermany

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