A fast Branch-and-Bound algorithm for U-curve feature selection

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dc.contributor(LCC) Laboratório de Ciclo Celularpt_BR
dc.contributorCentro de Toxinas, Resposta-imune e Sinalização Celular (CeTICS)pt_BR
dc.contributor.authorAtashpaz-Gargari, Esmaeilpt_BR
dc.contributor.authorReis, Marcelo da Silvapt_BR
dc.contributor.authorBraga-Neto, Ulisses M.pt_BR
dc.contributor.authorBarrera, Juniorpt_BR
dc.contributor.authorDougherty, Edward R.pt_BR
dc.date.accessioned2020-07-09T21:19:01Z-
dc.date.available2020-07-09T21:19:01Z-
dc.date.issued2018pt_BR
dc.identifier.citationAtashpaz-Gargari E, Reis MS, Braga-Neto UM., Barrera J, Dougherty ER.. A fast Branch-and-Bound algorithm for U-curve feature selection. Pattern Recognit. 2018 Jan; 73:172-88. doi:10.1016/j.patcog.2017.08.013.pt_BR
dc.identifier.urihttps://repositorio.butantan.gov.br/handle/butantan/2394-
dc.description.abstractWe introduce a fast Branch-and-Bound algorithm for optimal feature selection based on a U-curve assumption for the cost function. The U-curve assumption, which is based on the peaking phenomenon of the classification error, postulates that the cost over the chains of the Boolean lattice that represents the search space describes a U-shaped curve. The proposed algorithm is an improvement over the original algorithm for U-curve feature selection introduced recently. Extensive simulation experiments are carried out to assess the performance of the proposed algorithm (IUBB), comparing it to the original algorithm (UBB), as well as exhaustive search and Generalized Sequential Forward Search. The results show that the IUBB algorithm makes fewer evaluations and achieves better solutions under a fixed computational budget. We also show that the IUBB algorithm is robust with respect to violations of the U-curve assumption. We investigate the application of the IUBB algorithm in the design of imaging W-operators and in classification feature selection, using the average mean conditional entropy (MCE) as the cost function for the search.pt_BR
dc.description.sponsorship(FAPESP) Fundação de Amparo à Pesquisa do Estado de São Paulopt_BR
dc.format.extentp. 172-188pt_BR
dc.language.isoEnglishpt_BR
dc.relation.ispartofPattern Recognitionpt_BR
dc.rightsOpen accesspt_BR
dc.titleA fast Branch-and-Bound algorithm for U-curve feature selectionpt_BR
dc.typeArticlept_BR
dc.identifier.doi10.1016/j.patcog.2017.08.013pt_BR
dc.identifier.urlhttps://doi.org/10.1016/j.patcog.2017.08.013pt_BR
dc.contributor.externalTexas A&M University:ou00676pt_BR
dc.contributor.external(USP) Universidade de São Paulopt_BR
dc.identifier.citationvolume73pt_BR
dc.subject.keywordBranch-and-bound algorithmpt_BR
dc.subject.keywordFeature selectionpt_BR
dc.subject.keywordU-curve assumptionpt_BR
dc.subject.keywordW-operator designpt_BR
dc.relation.ispartofabbreviatedPattern Recognitpt_BR
dc.identifier.citationabntv. 73, p. 172-188, jan. 2018pt_BR
dc.identifier.citationvancouver2018 Jan; 73:172-88pt_BR
dc.contributor.butantanReis, Marcelo da Silva|:Pesquisador|:LCC - Laboratório de Ciclo Celular:Centro de Toxinas, Resposta-imune e Sinalização Celular (CeTICS)|:pt_BR
dc.sponsorship.butantan(FAPESP) Fundação de Amparo à Pesquisa do Estado de São Paulo¦¦2013/07467-1pt_BR
dc.sponsorship.butantan(FAPESP) Fundação de Amparo à Pesquisa do Estado de São Paulo¦¦2015/01587-0pt_BR
dc.identifier.bvsccBR78.1pt_BR
dc.identifier.bvsdbIBProdpt_BR
dc.description.dbindexedYespt_BR
item.grantfulltextembargo_29990101-
item.languageiso639-1English-
item.fulltextCom Texto completo-
item.openairetypeArticle-
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