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Case Base Reduction Using Solution-Space Metrics

  • Fei Ling Woon
  • Brian Knight
  • Miltos Petridis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2689)

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.

Keywords

Case Base Solution Space Good Predictive Power Removal Strategy Iris Dataset 
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 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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