Abstract
Automated knowledge acquisition is an important research issue in machine learning. There have been proposed several methods of inductive learning, such as ID3 family and AQ family. These methods are applied to discover meaningful knowledge from large database, and their usefulness is in some aspects ensured. However, in most of the cases, their methods are of deterministic nature, and reliability of the acquired knowledge is not evaluated statistically, which makes these methods ineffective when applied to the domain of essentially probabilistic nature, such as medical one. Extending concepts of rough set theory to probabilistic domain, we introduce a new approach to knowledge acquisition, which induces probabilistic rules based on rough set theory(PRIMEROSE) and develop a program that extracts rules for an expert system from clinical database, using this method. The results show that the derived rules almost correspond to those of medical experts.
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© 1994 British Computer Society
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Tsumoto, S., Tanaka, H. (1994). PRIMEROSE: Probabilistic Rule Induction Method Based on Rough Set Theory. 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_33
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DOI: https://doi.org/10.1007/978-1-4471-3238-7_33
Publisher Name: Springer, London
Print ISBN: 978-3-540-19885-7
Online ISBN: 978-1-4471-3238-7
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