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dc.contributor.author Richard en_US
dc.date.accessioned 2008-01-29T11:28:44Z
dc.date.available 2008-01-29T11:28:44Z
dc.date.issued 2006 en_US
dc.identifier.citation Jensen , R 2006 , ' Performing Feature Selection with ACO ' . in Swarm Intelligence and Data Mining . Springer Science+Business Media , pp. 45-73 . en_US
dc.identifier.other PURE: 74102 en_US
dc.identifier.other dspace: 2160/488 en_US
dc.identifier.uri http://hdl.handle.net/2160/488
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_US
dc.format.extent 29 en_US
dc.publisher Springer Science+Business Media en_US
dc.relation.ispartof Swarm Intelligence and Data Mining en_US
dc.title Performing Feature Selection with ACO en_US
dc.contributor.pbl Department of Computer Science en_US
dc.contributor.pbl Advanced Reasoning Group en_US


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