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Complexity Profiling for Informed Case-Base Editing

  • Stewart Massie
  • Susan Craw
  • Nirmalie Wiratunga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)

Abstract

The contents of the case knowledge container is critical to the performance of case-based classification systems. However the knowledge engineer is given little support in the selection of suitable techniques to maintain and monitor the case-base. In this paper we present a novel technique that provides an insight into the structure of a case-base by means of a complexity profile that can assist maintenance decision-making and provide a benchmark to assess future changes to the case-base. We also introduce a complexity-guided redundancy reduction algorithm which uses a local complexity measure to actively retain cases close to boundaries. The algorithm offers control over the balance between maintaining competence and reducing case-base size. The ability of the algorithm to maintain accuracy in a compacted case-base is demonstrated on seven public domain classification datasets.

Keywords

Decision Boundary Reduction Algorithm Relative Cover Knowledge Engineer Zero Complexity 
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

  • Stewart Massie
    • 1
  • Susan Craw
    • 1
  • Nirmalie Wiratunga
    • 1
  1. 1.School of ComputingThe Robert Gordon UniversityAberdeenScotland, UK

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