Evolutionary model trees for handling continuous classes in machine learning

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dc.contributor.authorBarros, Rodrigo C.
dc.contributor.authorRuiz, Duncan D.
dc.contributor.authorBasgalupp, Marcio P. [UNIFESP]
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionPontificia Univ Catolica Rio Grande do Sul
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.date.accessioned2016-01-24T14:06:16Z
dc.date.available2016-01-24T14:06:16Z
dc.date.issued2011-03-01
dc.description.abstractModel trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. in this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.en
dc.description.affiliationUniv São Paulo, BR-13560970 Sao Carlos, SP, Brazil
dc.description.affiliationPontificia Univ Catolica Rio Grande do Sul, BR-90619900 Porto Alegre, RS, Brazil
dc.description.affiliationUniversidade Federal de São Paulo, Inst Ciencia & Tecnol, BR-12231280 Sao Jose Dos Campos, Brazil
dc.description.affiliationUnifespUniversidade Federal de São Paulo, Inst Ciencia & Tecnol, BR-12231280 Sao Jose Dos Campos, Brazil
dc.description.sourceWeb of Science
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipEuropean Research Consortium for Informatics and Mathematics (ERCIM)
dc.format.extent954-971
dc.identifierhttp://dx.doi.org/10.1016/j.ins.2010.11.010
dc.identifier.citationInformation Sciences. New York: Elsevier B.V., v. 181, n. 5, p. 954-971, 2011.
dc.identifier.doi10.1016/j.ins.2010.11.010
dc.identifier.issn0020-0255
dc.identifier.urihttp://repositorio.unifesp.br/handle/11600/33527
dc.identifier.wosWOS:000286556700002
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofInformation Sciences
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.subjectEvolutionary algorithmsen
dc.subjectGenetic programmingen
dc.subjectModel treesen
dc.subjectContinuous classesen
dc.subjectMachine learningen
dc.titleEvolutionary model trees for handling continuous classes in machine learningen
dc.typeinfo:eu-repo/semantics/article
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