Show simple item record Galea, Michelle Shen, Qiang 2008-01-23T14:21:18Z 2008-01-23T14:21:18Z 2005
dc.identifier.citation Galea , M & Shen , Q 2005 , ' Iterative vs Simultaneous Fuzzy Rule Induction ' pp. 767-772 . DOI: 10.1109/FUZZY.2005.1452491 en
dc.identifier.other PURE: 74504
dc.identifier.other PURE UUID: 7d98ac1f-fe5d-4449-ac69-854f9516a7b5
dc.identifier.other dspace: 2160/469
dc.identifier.other DSpace_20121128.csv: row: 328
dc.identifier.other Scopus: 23944482202
dc.description M. Galea and Q. Shen. Iterative vs Simultaneous Fuzzy Rule Induction. Proceedings of the 14th International Conference on Fuzzy Systems, pages 767-772. 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 (ACO) and genetic algorithms. Several SPBAs are run in succession with the result of each being a rule added to an emerging final ruleset. Each successive rule is generally produced without taking into account the rules already in the final ruleset, and how well they may interact during fuzzy inference. This popular approach is compared with the simultaneous rule learning strategy introduced here, whereby the fuzzy rules that form the final ruleset are evolved and evaluated together. This latter strategy is found to maintain or improve classification accuracy of the evolved ruleset, and simplify the ACO algorithm used here as the rule discovery mechanism by removing the need for one parameter, and adding robustness to value changes in another. This initial work also suggests that the rulesets may be obtained at less computational expense than when using an iterative rule learning strategy. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof en
dc.rights en
dc.title Iterative vs Simultaneous Fuzzy Rule Induction 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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