Show simple item record

dc.contributor.authorBosio, Mattia
dc.contributor.authorBellot Pujalte, Pau
dc.contributor.authorSalembier Clairon, Philippe Jean
dc.contributor.authorOliveras Vergés, Albert
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2014-03-13T13:29:24Z
dc.date.created2013
dc.date.issued2013
dc.identifier.citationBosio, M. [et al.]. Ensemble learning and hierarchical data representation for microarray classification. A: IEEE International Conference on Bioinformatics and Bioengineering. "BIBE 2013: 13th IEEE International Conference on BioInformatics and BioEngineering: Chania, Greece: November 10-13, 2013". Chania: Institute of Electrical and Electronics Engineers (IEEE), 2013, p. 1-4.
dc.identifier.urihttp://hdl.handle.net/2117/22027
dc.description.abstractThe microarray data classification is an open and active research field. The development of more accurate algorithms is of great interest and many of the developed techniques can be straightforwardly applied in analyzing different kinds of omics data. In this work, an ensemble learning algorithm is applied within a classification framework that already got good predictive results. Ensemble techniques take individual experts, (i.e. classifiers), to combine them to improve the individual expert results with a voting scheme. In this case, a thinning algorithm is proposed which starts by using all the available experts and removes them one by one focusing on improving the ensemble vote. Two versions of a state of the art ensemble thinning algorithm have been tested and three key elements have been introduced to work with microarray data: the ensemble cohort definition, the nonexpert notion, which defines a set of excluded expert from the thinning process, and a rule to break ties in the thinning process. Experiments have been done on seven public datasets from the Microarray Quality Control study, MAQC. The proposed key elements have shown to be useful for the prediction performance and the studied ensemble technique shown to improve the state of the art results by producing classifiers with better predictions.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors::Protocols de comunicació
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subject.lcshBioinformatics
dc.subject.lcshBioengineering
dc.titleEnsemble learning and hierarchical data representation for microarray classification
dc.typeConference report
dc.subject.lemacBioinformàtica
dc.subject.lemacBioenginyeria
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6701647
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac13408389
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorBosio, M.; Bellot, P.; Salembier, P.; Oliveras, A.
local.citation.contributorIEEE International Conference on Bioinformatics and Bioengineering
local.citation.pubplaceChania
local.citation.publicationNameBIBE 2013: 13th IEEE International Conference on BioInformatics and BioEngineering: Chania, Greece: November 10-13, 2013
local.citation.startingPage1
local.citation.endingPage4


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder