From Anomaly Reports to Cases

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 competence of the system design.

• Type: Conference
• Year: 2007
• Where: ICCBR
• Authors: Stewart Massie, Nirmalie Wiratunga, Susan Craw, Alessandro Donati, Emmanuel Vicari
• Keywords: Case Representations, ICCBR 2007, Report Processing Task, Textual Case-based Reasoning