Abstract
Case-based reasoning is a knowledge processing concept that has shown success in various problem classes. One key challenge in CBR is the construction of a measure that adequately models the similarity between two cases. Typically, a similarity measure consists of a set of feature-specific distance functions coupled with an underlying feature weighting (importance) scheme. While the definition of the distance functions is often straightforward, the estimation of the weighting scheme requires a deep understanding of the domain and the underlying connections.
The paper in hand addresses this problem. It shows how discrimination knowledge, which is coded within an already solved classification problem, can be transformed towards a similarity measure. Moreover, it demonstrates our approach at the problem of diagnosing heart diseases.
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Stein, B., Niggemann, O., Husemeyer, U. (2000). Learning Complex Similarity Measures. In: Decker, R., Gaul, W. (eds) Classification and Information Processing at the Turn of the Millennium. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57280-7_28
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DOI: https://doi.org/10.1007/978-3-642-57280-7_28
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