Abstract
Problems of knowledge analysis for decision systems by means of the rough sets theory are considered in this paper. Knowledge coming from experience concerns classification of data and is represented in a form of an information system. Application of the rough sets theory to the analysis of information systems enables a reduction of superfluous information and the derivation of a decision algorithm. These results are used to support a classification of new facts. An idea of using “the nearest” rules to support this classification is presented.
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© 1993 Springer-Verlag Berlin Heidelberg
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Stefanowski, J. (1993). Classification Support Based on the Rough Sets Theory. In: Wessels, J., Wierzbicki, A.P. (eds) User-Oriented Methodology and Techniques of Decision Analysis and Support. Lecture Notes in Economics and Mathematical Systems, vol 397. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-22587-5_17
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DOI: https://doi.org/10.1007/978-3-662-22587-5_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-56382-2
Online ISBN: 978-3-662-22587-5
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