Abstract
We present a new knowledge learning method suitable for extracting symbolic rules from domains characterized by continuous domains. It uses the idea of competitive learning, symbolic rule reasoning and it integrates a statistical measure for relevance analysis during the learning process. The knowledge is in form of standard production rules which are available at any time during the learning process. The competition occurs among the rules for capturing a presented instance and the rules can undergo processes of merging, splitting, simplifying and deleting. Reasoning occurs at both higher level of abstraction and lower level of detail. The method is evaluated on publicly available real world datasets.
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© 2008 International Federation for Information Processing
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Hadzic, F., Dillon, T.S. (2008). Using Competitive Learning between Symbolic Rules as a Knowledge Learning Method. In: Bramer, M. (eds) Artificial Intelligence in Theory and Practice II. IFIP AI 2008. IFIP – The International Federation for Information Processing, vol 276. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09695-7_34
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DOI: https://doi.org/10.1007/978-0-387-09695-7_34
Publisher Name: Springer, Boston, MA
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