Performing Feature Selection with ACO

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