An adaptive case-based reasoning model for decision support in dynamic environments

In this research we propose an adaptive case-based reasoning model for the effective support of ill-structured, classificatory decisions. Our model is characterized by its incorporation of Type-I and Type-II errors into performance improvement. This model is used to complement the traditional approaches to decision support in dynamic environments. Performance comparison is conducted for our model, rule induction, and connectionist approaches.<
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