Complexity Profiling for Informed Case-Base Editing

Abstract. The contents of the case knowledge container is critical to the performance of case-based classification systems. However the knowledge engineer is given little support in the selection of suitable techniques to maintain and monitor the case-base. In this paper we present a novel technique that provides an insight into the structure of a case-base by means of a complexity profile that can assist maintenance decision-making and provide a benchmark to assess future changes to the case-base. We also introduce a complexity-guided redundancy reduction algorithm which uses a local complexity measure to actively retain cases close to boundaries. The algorithm offers control over the balance between maintaining competence and reducing case-base size. The ability of the algorithm to maintain accuracy in a compacted case-base is demonstrated on seven public domain classification datasets.

• Type: Conference
• Year: 2006
• Where: EWCBR
• Authors: Stewart Massie, Susan Craw, Nirmalie Wiratunga
• Keywords: Automated Reasoning, Case Knowledge Container, Case-based Classification Systems, EWCBR 2006, Redundancy Reduction Algorithm