Publication:
Optimizing data transformations for classification tasks

Simple item page
dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.contributor.authorValls, José M.
dc.contributor.authorAler, Ricardo
dc.date.accessioned2010-01-25T09:46:25Z
dc.date.available2010-01-25T09:46:25Z
dc.date.issued2009-09
dc.descriptionProceeding of: 10th International Conference, IDEAL 2009, Burgos, Spain, September 23-26, 2009
dc.description.abstractMany classification algorithms use the concept of distance or similarity between patterns. Previous work has shown that it is advantageous to optimize general Euclidean distances (GED). In this paper, data transformations are optimized instead. This is equivalent to searching for GEDs, but can be applied to any learning algorithm, even if it does not use distances explicitly. Two optimization techniques have been used: a simple Local Search (LS) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). CMA-ES is an advanced evolutionary method for optimization in difficult continuous domains. Both diagonal and complete matrices have been considered. Results show that in general, complete matrices found by CMA-ES either outperform or match both Local Search, and the classifier working on the original untransformed data.
dc.description.sponsorshipWork supported by project TIN2008-06491-C04-04 funded by Spanish MICINN
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationIntelligent Data Engineering and Automated Learning - IDEAL 2009 : 10th International Conference, Burgos, Spain, September 23-26, 2009. Springer, 2009, pp. 176-183
dc.identifier.doi10.1007/978-3-642-04394-9_22
dc.identifier.isbn978-3-642-04393-2
dc.identifier.issn0302-9743 (print)
dc.identifier.issn1611-3349 (Online)
dc.identifier.publicationfirstpage176
dc.identifier.publicationlastpage183
dc.identifier.publicationtitleIntelligent Data Engineering and Automated Learning - IDEAL 2009
dc.identifier.publicationvolume5788
dc.identifier.urihttps://hdl.handle.net/10016/6599
dc.language.isoeng
dc.publisherSpringer
dc.relation.eventdateSeptember 23-26, 2009
dc.relation.eventnumber10
dc.relation.eventplaceBurgos (Spain)
dc.relation.ispartofseriesLecture Notes in Computer Science, vol. 5788/
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-642-04394-9_22
dc.rights© Springer
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaInformática
dc.subject.otherData transformations
dc.subject.otherGeneral Euclidean distances
dc.subject.otherEvolutionary computation
dc.subject.otherEvolutionary-based machine learning
dc.titleOptimizing data transformations for classification tasks
dc.typeconference paper*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
IDEAL09.pdf
Size:
166.16 KB
Format:
Adobe Portable Document Format
Download
Collections
DI - GCERN - Comunicaciones en Congresos y otros eventos
DI - GCERN - Capítulos de Monografías