Abstract
One important aspect of Case-Based Reasoning (CBR) is Case Selection or Editing – selection for inclusion (or removal) of cases from a case base. This can be motivated either by space considerations or quality considerations. One of the advantages of CBR is that it is equally useful for boolean, nominal, ordinal, and numeric prediction tasks. However, many case selection research efforts have focused on domains with nominal or boolean predictions. Most case selection methods have relied on such problem structure. In this paper, we present details of a systematic sequence of experiments with variations on CBR case selection. In this project, the emphasis has been on case quality – an attempt to filter out cases that may be noisy or idiosyncratic – that are not good for future prediction. Our results indicate that Case Selection can significantly increase the percentage of correct predictions at the expense of an increased risk of poor predictions in less common cases.
Manuscript received October 12, 2009.
Both authors are with La Salle University, Philadelphia, PA 19141 USA (corresponding author M.A. Redmond phone: 215-951-1096; e-mail: redmond@ lasalle.edu).
1 Numeric prediction is called “regression” by some researchers, after the statistical technique long used for it
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Redmond, M.A., Highley, T. (2010). Empirical Analysis of Case-Editing Approaches for Numeric Prediction. In: Sobh, T., Elleithy, K. (eds) Innovations in Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9112-3_14
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DOI: https://doi.org/10.1007/978-90-481-9112-3_14
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