Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets

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dc.contributor.authorBarros, Rodrigo C.
dc.contributor.authorBasgalupp, Márcio Porto [UNIFESP]
dc.contributor.authorFreitas, Alex A.
dc.contributor.authorCarvalho, Andre C. P. L. F. de
dc.contributor.institutionPontificia Univ Catolica Rio Grande do Sul
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.contributor.institutionUniv Kent
dc.date.accessioned2016-01-24T14:38:12Z
dc.date.available2016-01-24T14:38:12Z
dc.date.issued2014-12-01
dc.description.abstractDecision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. the most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. in this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure.en
dc.description.affiliationPontificia Univ Catolica Rio Grande do Sul, Fac Informat, BR-90619900 Porto Alegre, RS, Brazil
dc.description.affiliationUniv São Paulo, Dept Comp Sci, BR-13566590 Sao Carlos, SP, Brazil
dc.description.affiliationUniversidade Federal de São Paulo, Inst Ciencia & Tecnol, BR-12231280 Sao Jose Dos Campos, Brazil
dc.description.affiliationUniv Kent, Dept Comp Sci, Canterbury CT2 7NF, Kent, England
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.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIDFAPESP: 2009/14325-3
dc.format.extent873-892
dc.identifierhttp://dx.doi.org/10.1109/TEVC.2013.2291813
dc.identifier.citationIeee Transactions On Evolutionary Computation. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 18, n. 6, p. 873-892, 2014.
dc.identifier.doi10.1109/TEVC.2013.2291813
dc.identifier.fileWOS000345907800007.pdf
dc.identifier.issn1089-778X
dc.identifier.urihttp://repositorio.unifesp.br/handle/11600/38473
dc.identifier.wosWOS:000345907800007
dc.language.isoeng
dc.publisherIeee-inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions On Evolutionary Computation
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dc.subjectAutomatic algorithm designen
dc.subjectclassificationen
dc.subjectdecision treesen
dc.subjectevolutionary algorithmsen
dc.subjecthyper-heuristicsen
dc.subjectmachine learningen
dc.titleEvolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Setsen
dc.typeinfo:eu-repo/semantics/article
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