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CBTV: Visualising Case Bases for Similarity Measure Design and Selection

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Case-Based Reasoning. Research and Development (ICCBR 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6176))

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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.

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Author information

Authors and Affiliations

  1. DIT AI Group, Dublin Institute of Technology, Dublin, Ireland

    Brian Mac Namee & Sarah Jane Delany

Authors
  1. Brian Mac Namee
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  2. Sarah Jane Delany
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Editor information

Editors and Affiliations

  1. Institute of Technology, University of Washington, Tacoma, 1900 Commerce Street, Box 358426, 98402, Tacoma, WA, USA

    Isabelle Bichindaritz

  2. Dipartimento di Informatica, Università del Piemonte Orientale, P.O. Box, Alessandria, Italy

    Stefania Montani

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Namee, B.M., Delany, S.J. (2010). CBTV: Visualising Case Bases for Similarity Measure Design and Selection. In: Bichindaritz, I., Montani, S. (eds) Case-Based Reasoning. Research and Development. ICCBR 2010. Lecture Notes in Computer Science(), vol 6176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14274-1_17

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  • DOI: https://doi.org/10.1007/978-3-642-14274-1_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14273-4

  • Online ISBN: 978-3-642-14274-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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