Show simple item record Singh, Vishal Shen, Qiang Galea, Michelle 2008-01-23T09:49:54Z 2008-01-23T09:49:54Z 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 PURE UUID: 6dd9610a-b060-4451-a366-12f6ce91b7e9
dc.identifier.other dspace: 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.rights en
dc.title Encouraging Complementary Fuzzy Rules within Iterative Rule Learning en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontoconference/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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