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- W2246258145 abstract "Bayesian case-based reasoning has at its core a Bayesian network model for memory, retrieval, indexing and organization. In retrieval, a query network is constructed with the query as the root and features as the leaf nodes. We select the case that maximizes posterior probability of the query. In the presence on non-informative features, dependent features or noisy cases, we use adaptive methods such as feature selection, feature dependency analysis and representative cases selection to refine the Bayesian network model. Both feature selection and feature dependency analysis use a cross-validation scheme for evaluating different feature combinations and the back and forth hill-climbing search strategy. As a result, predictive accuracy of retrieval can be greatly improved via adaptation. As for indexing, conceptual clustering methods can be used to generate new indices for new and stored cases. We propose an iterative refinement clustering procedure. This procedure helps to avoid stopping at local minimal of performance functions too early. After labels of some new instances are assigned, clustering iteratively adjusts contents of clusters by re-calculating class probability for instances of some clusters. As a result, misplaced instances are minimized. The number of desired indices is the maximum of probabilities of a number given all stored instances Since the condition of the clusters is not known, it is viewed as a hidden variable. Thus, the final case organization is represented by a Bayesian network with hidden variables. Our implemented system has been used as a testbed for studying CBR functions." @default.
- W2246258145 created "2016-06-24" @default.
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- W2246258145 date "1996-01-01" @default.
- W2246258145 modified "2023-09-23" @default.
- W2246258145 title "Case-Based Retrieval and Indexing Using a Bayesian Network Model" @default.
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