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dc.contributor.author Singh, Vishal
dc.contributor.author Shen, Qiang
dc.contributor.author Galea, Michelle
dc.date.accessioned 2008-01-23T09:49:54Z
dc.date.available 2008-01-23T09:49:54Z
dc.date.issued 2005
dc.identifier.citation Singh , V , Shen , Q & Galea , M 2005 , ' Encouraging Complementary Fuzzy Rules within Iterative Rule Learning ' pp. 15-22 . en
dc.identifier.other PURE: 74876
dc.identifier.other dspace: 2160/459
dc.identifier.uri http://hdl.handle.net/2160/459
dc.description M. Galea, Q. Shen and V. Singh. Encouraging Complementary Fuzzy Rules within Iterative Rule Learning. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 15-22. en
dc.description.abstract Iterative rule learning is a common strategy for fuzzy rule induction using stochastic population-based algorithms (SPBAs) such as Ant Colony Optimisation and genetic algorithms. Several SPBAs are run in succession with the result of each being a rule added to an emerging final ruleset. Between SPBA runs, cases in the training set that are covered by the newly evolved rule are generally removed, so as to encourage the next SPBA to find good rules describing the remaining cases. This paper compares this IRL variant with another variant that instead weights cases between iterations. The latter approach results in improved classification accuracy and an increased robustness to parameter value changes. en
dc.format.extent 8 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Encouraging Complementary Fuzzy Rules within Iterative Rule Learning en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en


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