Abstract
This paper focuses on the application of rough set constructs to inductive learning from a database. A design guideline is suggested, which provides users the option to choose appropriate attributes, for the construction of classification rules. Error probabilities for the resultant rule are derived. A classification rule can be further generalized using concept hierarchies. The condition for preventing overgeneralization is derived. Moreover, given a constraint, an algorithm for generating a rule with minimal error probability is proposed.
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© 1994 British Computer Society
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Xiang, Y., Wong, S.K.M., Cercone, N. (1994). Quantifying Uncertainty of Knowledge Discovered From Databases. In: Ziarko, W.P. (eds) Rough Sets, Fuzzy Sets and Knowledge Discovery. Workshops in Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3238-7_8
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DOI: https://doi.org/10.1007/978-1-4471-3238-7_8
Publisher Name: Springer, London
Print ISBN: 978-3-540-19885-7
Online ISBN: 978-1-4471-3238-7
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