An Evolutionary Multiobjective Constrained Optimisation Approach for Case Selection: Evaluation in a Medical Problem

  • Eduardo Lupiani
  • Fernando Jimenez
  • José M. Juarez
  • José Palma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7023)


A solid building process and a good evaluation of the knowledge base are essential in the clinical application of Case-Based Reasoning Systems. Unlike other approaches, each piece of the knowledge base (cases of the case memory) is knowledge-complete and independent from the rest. Therefore, the main issue to build a case memory is to select which cases must be included or removed. Literature provides a wealth of methods based on instance selection from a database. However, it can be also understood as a multiobjective problem, maximising the accuracy of the system and minimising the number of cases in the case memory. Most of the efforts done in this evaluation of case selection methods focus on the number of registers selected, providing an evaluation of the system based on its accuracy. On the one hand, some case selection methods follow a non deterministic approach. Therefore, a rough evaluation could entail to inaccurate conclusions. On the other hand, specificity and sensitivity are critical values to evaluate tests in the medical field. However, these parameters are hardly ever included in the case selection evaluation. In order to partially solve this problem, we propose an evaluation methodology to obtain the best case selection method for a given memory case. We also propose a case selection method based on multiobjective constrained optimisation for which Evolutionary Algorithms are used. Finally, we illustrate the use of this methodology by evaluating classic and the case selection method proposed, in a particular problem of Burn Intensive Care Units.


Multiobjective Optimisation Case Selection Evaluation Methodology Multiobjective Problem Multiobjective Evolutionary Algorithm 
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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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Eduardo Lupiani
    • 1
  • Fernando Jimenez
    • 1
  • José M. Juarez
    • 1
  • José Palma
    • 1
  1. 1.Computer Science FacultyUniversidad de MurciaSpain

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