Abstract
One of the key technologies of data mining is the automatic induction of rules from examples, particularly the induction of classification rules. Most work in this field has concentrated on the generation of such rules in the intermediate form of decision trees. An alternative approach is to generate modular classification rules directly from the examples. This paper seeks to establish a revised form of the rule generation algorithm Prism as a credible candidate for use in the automatic induction of classification rules from examples in practical domains where noise may be present and where predicting the classification for previously unseen instances is the primary focus of attention.
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Bramer, M. (2000). Automatic Induction of Classification Rules from Examples Using N-Prism. In: Bramer, M., Macintosh, A., Coenen, F. (eds) Research and Development in Intelligent Systems XVI. Springer, London. https://doi.org/10.1007/978-1-4471-0745-3_7
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DOI: https://doi.org/10.1007/978-1-4471-0745-3_7
Publisher Name: Springer, London
Print ISBN: 978-1-85233-231-0
Online ISBN: 978-1-4471-0745-3
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