CBTV: visualising case bases for similarity measure design and selection

Authors:
Brian Mac Namee;Sarah Jane Delany
Affiliations:
DIT AI Group, Dublin Institute of Technology, Dublin, Ireland;DIT AI Group, Dublin Institute of Technology, Dublin, Ireland
Venue:
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Year:
2010

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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.