Abstract
An algorithm for learning ripple down rules, that is rules with hierarchical exceptions, is presented. The algorithm is generic with respect to the set of possible conditions; conditions are manipulated by an abstract generalization operator only. A specialization of the algorithm is shown that learns classification rules in real-valued attribute space; it is compared to other machine learning, neural network, and statistical algorithms. Learning algorithms for graphs or first order logics can be derived as well.
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© 1995 Springer-Verlag Berlin Heidelberg
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Scheffer, T. (1995). A generic algorithm for learning rules with hierarchical exceptions. In: Wainer, J., Carvalho, A. (eds) Advances in Artificial Intelligence. SBIA 1995. Lecture Notes in Computer Science, vol 991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0034811
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DOI: https://doi.org/10.1007/BFb0034811
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