Inductive Learning for Case-Based Diagnosis
with Multiple Faults

Joachim Baumeister, Martin Atzmller, and Frank Puppe

University of Wrzburg, 97074 Wrzburg, Germany
Department of Computer Science
Phone:	+49 931 888-6740, Fax: +49 931 888-6732
{baumeister,atzmueller,puppe}@informatik.uni-wuerzburg.de



Abstract. 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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