Case Base Reduction Using Solution-Space Metrics

  • Fei Ling Woon
  • Brian Knight
  • Miltos Petridis
Conference paper

DOI: 10.1007/3-540-45006-8_49

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2689)
Cite this paper as:
Woon F.L., Knight B., Petridis M. (2003) Case Base Reduction Using Solution-Space Metrics. In: Ashley K.D., Bridge D.G. (eds) Case-Based Reasoning Research and Development. ICCBR 2003. Lecture Notes in Computer Science, vol 2689. Springer, Berlin, Heidelberg

Abstract

In this paper we propose a case base reduction technique which uses a metric defined on the solution space. The technique utilises the Generalised Shepard Nearest Neighbour (GSNN) algorithm to estimate nominal or real valued solutions in case bases with solution space metrics. An overview of GSNN and a generalised reduction technique, which subsumes some existing decremental methods, such as the Shrink algorithm, are presented. The reduction technique is given for case bases in terms of a measure of the importance of each case to the predictive power of the case base. A trial test is performed on two case bases of different kinds, with several metrics proposed in the solution space. The tests show that GSNN can out-perform standard nearest neighbour methods on this set. Further test results show that a caseremoval order proposed based on a GSNN error function can produce a sparse case base with good predictive power.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Fei Ling Woon
    • 1
  • Brian Knight
    • 2
  • Miltos Petridis
    • 2
  1. 1.Tunku Abdul Rahman CollegeSchool of Arts and ScienceKuala LumpurMalaysia
  2. 2.University of GreenwichSchool of Computing and Mathematical SciencesLondonUK

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