Abstract
We present a method for inducing classification rules from imperfect data using an extended version of the rough set model. The salient feature of our method is that it makes use of the statistical information inherent in the information system. Our framework describes the overall induction task in terms of two key subtasks: approximate classification and rule generation.
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References
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© 1996 Springer-Verlag Berlin Heidelberg
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Shan, N., Hamilton, H.J., Cercone, N. (1996). Induction of classification rules from imperfect data. In: Raś, Z.W., Michalewicz, M. (eds) Foundations of Intelligent Systems. ISMIS 1996. Lecture Notes in Computer Science, vol 1079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61286-6_137
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DOI: https://doi.org/10.1007/3-540-61286-6_137
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