Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23607
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dc.contributor.authorNeumann, Geoffrey-
dc.contributor.authorCairns, David-
dc.contributor.editorMadani, K-
dc.contributor.editorDourado, A-
dc.contributor.editorRosa, A-
dc.contributor.editorFilipe, J-
dc.contributor.editorKacprzyk, J-
dc.date.accessioned2016-12-15T04:09:19Z-
dc.date.issued2016-
dc.identifier.urihttp://hdl.handle.net/1893/23607-
dc.description.abstractThe Targeted Estimation of Distribution Algorithm (TEDA) introduces into an EDA/GA hybrid framework a ‘Targeting’ process, whereby the number of active genes, or ‘control points’, in a solution is driven in an optimal direction. For larger feature selection problems with over a thousand features, traditional methods such as forward and backward selection are inefficient. Traditional EAs may perform better but are slow to optimize if a problem is sufficiently noisy that most large solutions are equally ineffective and it is only when much smaller solutions are discovered that effective optimization may begin. By using targeting, TEDA is able to drive down the feature set size quickly and so speeds up this process. This approach was tested on feature selection problems with between 500 and 20,000 features using all of these approaches and it was confirmed that TEDA finds effective solutions significantly faster than the other approaches.en_UK
dc.language.isoen-
dc.publisherSpringer-
dc.relationNeumann G & Cairns D (2016) A Targeted Estimation of Distribution Algorithm Compared to Traditional Methods in Feature Selection In: Madani K, Dourado A, Rosa A, Filipe J, Kacprzyk J (ed.) Computational Intelligence: Revised and Selected Papers of the International Joint Conference, IJCCI 2013, Vilamoura, Portugal, September 20-22, 2013, Cham, Switzerland: Springer. 5th International Joint Conference on Computational Intellegience, IJCCI 2013, 20.9.2013 - 22.9.2013, Vilamoura, Portugal, pp. 83-103.-
dc.relation.ispartofseriesStudies in Computational Intelligence, 613-
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.-
dc.subjectEstimation of distribution algorithmsen_UK
dc.subjectFeature selectionen_UK
dc.subjectEvolutionary computationen_UK
dc.subjectGenetic algorithmsen_UK
dc.subjectHybrid algorithmsen_UK
dc.titleA Targeted Estimation of Distribution Algorithm Compared to Traditional Methods in Feature Selectionen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate2999-12-31T00:00:00Z-
dc.rights.embargoreasonThe publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.-
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-23392-5_5-
dc.citation.issn1860-949X-
dc.citation.spage83-
dc.citation.epage103-
dc.citation.publicationstatusPublished-
dc.citation.peerreviewedRefereed-
dc.type.statusBook Chapter: publisher version-
dc.identifier.urlhttp://link.springer.com/chapter/10.1007/978-3-319-23392-5_5-
dc.author.emaildec@cs.stir.ac.uk-
dc.citation.btitleComputational Intelligence: Revised and Selected Papers of the International Joint Conference, IJCCI 2013, Vilamoura, Portugal, September 20-22, 2013-
dc.citation.conferencedates2013-09-20T00:00:00Z-
dc.citation.conferencelocationVilamoura, Portugal-
dc.citation.conferencename5th International Joint Conference on Computational Intellegience, IJCCI 2013-
dc.citation.date09/2013-
dc.citation.isbn978-3-319-23392-5-
dc.citation.isbn978-3-319-23391-8-
dc.publisher.addressCham, Switzerland-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.rights.embargoterms2999-12-31-
dc.rights.embargoliftdate2999-12-31-
dc.identifier.isi000377543600005-
Appears in Collections:Computing Science and Mathematics Book Chapters and Sections

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