Performing Feature Selection with ACO

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dc.contributor.author Jensen, Richard
dc.date.accessioned 2008-01-29T11:28:44Z
dc.date.available 2008-01-29T11:28:44Z
dc.date.issued 2006
dc.identifier.citation Jensen , R 2006 , ' Performing Feature Selection with ACO ' . in Swarm Intelligence and Data Mining . Springer Verlag , pp. 45-73 . en
dc.identifier.other PURE: 74102
dc.identifier.other dspace: 2160/488
dc.identifier.uri http://hdl.handle.net/2160/488
dc.identifier.uri http://www.springer.com/east/home/computer?SGWID=5-146-22-173662727-0 en
dc.description R. Jensen, 'Performing Feature Selection with ACO. Swarm Intelligence and Data Mining,' A. Abraham, C. Grosan and V. Ramos (eds.), Studies in Computational Intelligence, vol. 34, pp. 45-73. 2006. en
dc.description.abstract The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In real world problems FS is a must due to the abundance of noisy, irrelevant or misleading features. However, current methods are inadequate at finding optimal reductions. This chapter presents a feature selection mechanism based on Ant Colony Optimization in an attempt to combat this. The method is then applied to the problem of finding optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to two very different challenging tasks, namely web classification and complex systems monitoring. en
dc.format.extent 29 en
dc.language.iso eng
dc.publisher Springer Verlag
dc.relation.ispartof Swarm Intelligence and Data Mining en
dc.title Performing Feature Selection with ACO en
dc.type Text en
dc.type.publicationtype Book chapter en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en


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