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 i s measured at a local level and profiles of these local alignments used to evaluate the competence of the system design. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Massie, S., Wiratunga, N., Craw, S., Donati, A., & Vicari, E. (2007). From anomaly reports to cases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4626 LNAI, pp. 359–373). Springer Verlag. https://doi.org/10.1007/978-3-540-74141-1_25
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