Reexamination of CBR Hypothesis

Most of the recent literature on complexity measures in textual case-based reasoning examined alignment between problem space and solution space, which used to be an issue of formulating CBR hypothesis. However, none of existing complexity measures could dispel the specter of predefined class label that does not appear in public textual datasets available, or clarify the correctness of the proposed solutions in the retrieved cases most similar to a target problem. This paper presented a novel alignment measure to circumvent these difficulties by calculating rank correlation between most similar case rankings in problem space and most similar case rankings in solution space. We also examined how to utilize existing alignment measures for textual case retrieval and textual case base maintenance. Empirical evaluation on Aviation Investigation Reports from Transportation Safety Board of Canada showed that rank correlation alignment measure might become a promising method for case-based non-classification systems