Summary
The problem being addressed is that of classification support using decision rules learned from examples. The learning methodology is based on the rough set theory which is particularly well suited to deal with inconsistencies in the set of examples. The rules produced using the rough set theory are categorized into deterministic and non-deterministic depending whether a condition part of the rule is uniquely related with a decision part or not. The classification support is performed by matching a new case to one of decision rules. A possible result is that the new case does not match any of the known rules. Then, a set of rules “nearest” to the description of the new case is presented to the decision maker. In order to find “nearest” rules, a distance measure based on a valued closeness relation, accepting both nominal and ordinal attributes, is used. A medical example illustrates the classification support.
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© 1994 Springer-Verlag Berlin Heidelberg
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Słowiński, R., Stefanowski, J. (1994). Rough Classification with Valued Closeness Relation. In: Diday, E., Lechevallier, Y., Schader, M., Bertrand, P., Burtschy, B. (eds) New Approaches in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-51175-2_56
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DOI: https://doi.org/10.1007/978-3-642-51175-2_56
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