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Abstract: Objectives: Balancing the trade-offs between solution quality, problem-solving efficiency, and transparency is an important challenge in medical applications of conversational case-based reasoning (CCBR). For example, test selection in CCBR is often based on strategies in which the absence of a specific hypothesis (e.g., diagnosis) to be confirmed makes it difficult to explain the relevance of test results that users are asked to provide. In this paper, we present an approach to CCBR in medical classification and diagnosis that aims to increase transparency while also providing high levels of accuracy and efficiency. Methods: We present an algorithm for CCBR called iNN(k) in which feature selection is driven by the goal of confirming a target class and informed by a measure of a feature''s discriminating power in favor of the target class. As we demonstrate in a CCBR system called CBR-Confirm, this enables a CCBR system to explain the relevance of any question it asks the user. We evaluate the algorithm''s accuracy and efficiency on a selection of datasets related to medicine and health care. Results: The performance of iNN(k) on a given dataset is shown to depend on the value of k and on whether local or global feature selection is used in the algorithm. The combination of these parameters for which iNN(k) is most effective in addressing the trade-off between accuracy and efficiency is identified for each of the selected datasets. For example, only 42% and 51% on average of features in a complete problem description were needed by iNN(k) to provide accuracy levels of 86.5% and 84.3% respectively on the lymphography and SPECT heart datasets from the UCI machine learning repository. Conclusion: Our results demonstrate the ability of iNN(k) to provide high levels of accuracy on most of the selected datasets, while often requiring the user to provide only a small subset of the features in a complete problem description, and enabling a CCBR system to explain the relevance of any question it asks the user. [Copyright &y& Elsevier]

Coverage-optimised retrieval (CORE) is a new case-based reasoning (CBR) approach to product recommendation which ensures that for any case that is acceptable to the user, one of the recommended cases is at least as good in an objective sense and so also likely to be acceptable. Similarity to the users query, the standard criterion on which recommendations are based in CBR, is only one of several preference criteria according to which a given case may be considered at least as good as another in CORE. We present a detailed analysis of retrieval in CORE and the trade-off between user satisfaction and cognitive load in the approach. [Copyright &y& Elsevier]

In applications of interactive case-based reasoning (CBR) such as help-desk support and recommender systems, a problem that often affects retrieval performance is the inability to distinguish between cases that have different solutions. For example, it is not unusual in recommender systems for two distinct products or services to have the same values for all attributes in the case library. While it is unlikely that both solutions are equally suited to the user''s requirements, the system cannot help the user to choose between them. This problem, which we refer to as inseparability, can also arise as a result of incomplete data in the target problem presented for solution by a CBR system. We present an in-depth analysis of the inseparability problem, its relationship to the problem of incomplete data, and its impact on retrieval performance. [Copyright &y& Elsevier]

CaseMaker is an interactive environment for intelligent case-authoring support in CREST, a case-based reasoner for estimation tasks, in which the selection of cases for addition to a case library is guided by empirical evaluation of the coverage contributions of candidate cases. I present a new version of the environment called CaseMaker-2 that is designed to support case authoring more effectively by eliminating a previous requirement for the evaluation of candidate cases to be repeated following the addition of a new case to the library. A key role in the approach is played by an algorithm for dynamic partitioning of the space of uncovered cases in such a way that the partition containing a given case represents the minimum additional coverage provided by its addition to the case library. [ABSTRACT FROM AUTHOR]