Abstract
An essential issue for developing analogical reasoning systems (such as Case-Based Reasoning systems) is to build the case memory by selecting registers from an external database. This issue is called case selection and the literature provides a wealth of algorithms to deal with it. For any particular domain, to choose the case selection algorithm is a critical decision on the system design. Despite some algorithms obtain good results, a specific algorithms evaluation is needed. Most of the efforts done in this line focus on the number of registers selected and providing a simple evaluation of the system obtained. In some domains, however, the system must fulfil certain constraints related to accuracy or efficiency. For instance, in the medical field, specificity and sensitivity are critical values for some tests. In order to partially solve this problem, we propose an evaluation methodology to obtain the best case selection method for a given memory case. In order to demonstrate the usefulness of this methodology, we present new case selection algorithms based on evolutionary multi-objective optimization. We compare the classical algorithms and the multi-objective approach in order to select the most suitable case selection algorithm according to different standard problems.
This study was partially financed by the Spanish MEC through projects TIN2009-14372-C03-01 and PET2007-0033 and the regional project SENECA 15277/PI/10.
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Lupiani, E., Juarez, J.M., Jimenez, F., Palma, J. (2011). Evaluating Case Selection Algorithms for Analogical Reasoning Systems. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) Foundations on Natural and Artificial Computation. IWINAC 2011. Lecture Notes in Computer Science, vol 6686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21344-1_36
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