CBR Outcome Evaluation for High Similar Cases: A Preliminary Approach

  • José M. Juarez
  • Manuel Campos
  • Antonio Gomariz
  • José T. Palma
  • Roque Marín
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5988)


Case-based reasoning has demonstrated to be a suitable similarity-based approach to develop decision-support system in different domains. However, in certain scenarios CBR finds difficulties to obtain a reliable solution when retrieved cases are highly similar. For example, patients from an Intensive Care Unit are critical patients in which slight variations of monitored parameters have a deep impact on the patient severity evaluation. In this scenario, it seems necessary to extend the system outcome in order to indicate the reliance of the solution obtained. Main efforts in the literature for CBR evaluation focus on case retrieval (i.e. similarity) or a retrospective analysis. However, these approaches do not seem to suffice when cases are very close. To this end, we propose three techniques to obtain a reliance solution degree, one based on case retrieval and two based on case adaptation. We also show the capacities of this proposal in a medical problem.


Case Base Reasoning Adaptation Method Correction Score Adaptation Step Global Similarity 
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 2010

Authors and Affiliations

  • José M. Juarez
    • 1
  • Manuel Campos
    • 2
  • Antonio Gomariz
    • 1
  • José T. Palma
    • 1
  • Roque Marín
    • 1
  1. 1.Information and Communication Engineering DepartmentUniversity of MurciaSpain
  2. 2.Computer Science and Systems DepartmentUniversity of MurciaSpain

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