Show simple item record Shen, Qiang Galea, Michelle 2008-01-23T10:01:00Z 2008-01-23T10:01:00Z 2004
dc.identifier.citation Shen , Q & Galea , M 2004 , ' Evolutionary approaches to fuzzy modelling for classification ' pp. 27-59 . DOI: 10.1017/S0269888904000189 en
dc.identifier.other PURE: 74606
dc.identifier.other PURE UUID: f9163d31-6e64-4615-ba63-429b8c4d580c
dc.identifier.other dspace: 2160/461
dc.identifier.other DSpace_20121128.csv: row: 333
dc.identifier.other Scopus: 18744394682
dc.description M. Galea, Q. Shen and J. Levine. Evolutionary approaches to fuzzy modelling. Knowledge Engineering Review, 19(1):27-59, 2004. en
dc.description.abstract An overview of the application of evolutionary computation to fuzzy knowledge discovery is presented. This is set in one of two contexts: overcoming the knowledge acquisition bottleneck in the development of intelligent reasoning systems, and in the data mining of databases where the aim is the discovery of new knowledge. The different strategies utilizing evolutionary algorithms for knowledge acquisition are abstracted from the work reviewed. The simplest strategy runs an evolutionary algorithm once, while the iterative rule learning approach runs several evolutionary algorithms in succession, with the output from each considered a partial solution. Ensembles are formed by combining several classifiers generated by evolutionary techniques, while co-evolution is often used for evolving rule bases and associated membership functions simultaneously. The associated strengths and limitations of these induction strategies are compared and discussed. Ways in which evolutionary techniques have been adapted to satisfy the common evaluation criteria of the induced knowledge—classification accuracy, comprehensibility and novelty value—are also considered. The review concludes by highlighting common limitations of the experimental methodology used and indicating ways of resolving them. en
dc.format.extent 33 en
dc.language.iso eng
dc.relation.ispartof en
dc.rights en
dc.title Evolutionary approaches to fuzzy modelling for classification 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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