Fuzzy rules from ant-inspired computation


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dc.contributor.author Shen, Qiang
dc.contributor.author Galea, Michelle
dc.date.accessioned 2008-01-22T16:57:58Z
dc.date.available 2008-01-22T16:57:58Z
dc.date.issued 2004
dc.identifier.citation Shen , Q & Galea , M 2004 , ' Fuzzy rules from ant-inspired computation ' pp. 1691-1696 . en
dc.identifier.other PURE: 74705
dc.identifier.other dspace: 2160/452
dc.identifier.uri http://hdl.handle.net/2160/452
dc.identifier.uri http://ieeexplore.ieee.org/Xplore/login.jsp?url=/iel5/9458/30018/01375435.pdf?tp=&arnumber=1375435&isnumber=30018 en
dc.description M. Galea and Q. Shen. Fuzzy rules from ant-inspired computation. Proceedings of the 13th International Conference on Fuzzy Systems, pages 1691-1696, 2004. en
dc.description.abstract A new approach to fuzzy rule induction from historical data is presented. The implemented system - FRANTIC - is a tested on a simple classification problem against a fuzzy tree induction algorithm, a genetic algorithm, and a numerical method for inducing fuzzy rules based on fuzzy subsethood values. The results obtained by FRANTIC indicate comparable or better classification accuracy, superior comprehensibility, and potentially more flexibility when applied to larger data sets. The impact of the knowledge representation used when generating fuzzy rules is also highlighted. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Fuzzy rules from ant-inspired computation 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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