From Anomaly Reports to Cases

  • Authors:
  • Stewart Massie;Nirmalie Wiratunga;Susan Craw;Alessandro Donati;Emmanuel Vicari

  • Affiliations:
  • School of Computing, The Robert Gordon University, Aberdeen AB25 1HG, Scotland, UK;School of Computing, The Robert Gordon University, Aberdeen AB25 1HG, Scotland, UK;School of Computing, The Robert Gordon University, Aberdeen AB25 1HG, Scotland, UK;European Space Agency, European Space Operations Centre, 64293 Darmstadt, Germany;European Space Agency, European Space Operations Centre, 64293 Darmstadt, Germany

  • Venue:
  • ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
  • Year:
  • 2007

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Abstract

Creating case representations in unsupervised textual case-based reasoning applications is a challenging task because class knowledge is not available to aid selection of discriminatory features or to evaluate alternative system design configurations. Representation is considered as part of the development of a tool, called CAM, which supports an anomaly report processing task for the European Space Agency. Novel feature selection/extraction techniques are created which consider word co-occurrence patterns to calculate similarity between words. These are used together with existing techniques to create 5 different case representations. A new evaluation technique is introduced to compare these representations empirically, without the need for expensive, domain expert analysis. Alignment between the problem and solution space is measured at a local level and profiles of these local alignments used to evaluate the competenceof the system design.