CBTV: Visualising case bases for similarity measure design and selection

Brian Mac Namee, Sarah Jane Delany

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationCase-Based Reasoning Research and Development - 18th International Conference on Case-Based Reasoning, ICCBR 2010, Proceedings
Pages213-227
Number of pages15
DOIs
Publication statusPublished - 2010
Event18th International Conference on Case-Based Reasoning, ICCBR 2010 - Alessandria, Italy
Duration: 19 Jul 201022 Jul 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6176 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Case-Based Reasoning, ICCBR 2010
Country/TerritoryItaly
CityAlessandria
Period19/07/1022/07/10

Keywords

  • CBR
  • similarity measures
  • exploratory visualisation
  • cross-validation accuracy comparison
  • Case Base Topology Viewer
  • datasets
  • decision boundary space

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