Evaluating CBR Systems Using Different Data Sources: A Case Study

  • Mingyang Gu
  • Agnar Aamodt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)


The complexity and high construction cost of case bases make it very difficult, if not impossible, to evaluate a CBR system, especially a knowledge-intensive CBR system, using statistical evaluation methods on many case bases. In this paper, we propose an evaluation strategy, which uses both many simple case bases and a few complex case bases to evaluate a CBR system, and show how this strategy may satisfy different evaluation goals. The identified evaluation goals are classified into two categories: domain-independent and domain-dependent. For the evaluation goals in the first category, we apply the statistical evaluation method using many simple case bases (for example, UCI data sets); for evaluation goals in the second category, we apply different, relatively weak, evaluation methods on a few complex domain-specific case bases. We apply this combined evaluation strategy to evaluate our knowledge-intensive conversational CBR method as a case study.


Case Base Evaluation Goal Target Case Case Retrieval General Domain Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mingyang Gu
    • 1
  • Agnar Aamodt
    • 1
  1. 1.Department of Computer and Information ScienceNorwegian University of Science and TechnologyTrondheimNorway

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