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CBTV: Visualising Case Bases for Similarity Measure Design and Selection

  • Brian Mac Namee
  • Sarah Jane Delany
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6176)

Abstract

In CBR the design and selection of similarity measures is paramount. Selection can benefit from the use of exploratory visualisation-based techniques in parallel with techniques such as cross-validation accuracy comparison. In this paper we present the Case Base Topology Viewer (CBTV) which allows the application of different similarity measures to a case base to be visualised so that system designers can explore the case base and the associated decision boundary space. We show, using a range of datasets and similarity measure types, how the idiosyncrasies of particular similarity measures can be illustrated and compared in CBTV allowing CBR system designers to make more informed choices.

Keywords

Similarity Measure Case Base Cosine Similarity Normalize Compression Distance High Dimensional Version 
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 2010

Authors and Affiliations

  • Brian Mac Namee
    • 1
  • Sarah Jane Delany
    • 1
  1. 1.DIT AI GroupDublin Institute of TechnologyDublinIreland

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