Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms

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dc.contributor.author Barros, Rodrigo C.
dc.contributor.author Basgalupp, Marcio P. [UNIFESP]
dc.contributor.author Carvalho, Andre C. P. L. F. de
dc.contributor.author Freitas, Alex A.
dc.date.accessioned 2016-01-24T14:34:42Z
dc.date.available 2016-01-24T14:34:42Z
dc.date.issued 2013-11-01
dc.identifier http://dx.doi.org/10.1162/EVCO_a_00101
dc.identifier.citation Evolutionary Computation. Cambridge: Mit Press, v. 21, n. 4, p. 659-684, 2013.
dc.identifier.issn 1063-6560
dc.identifier.uri http://repositorio.unifesp.br/handle/11600/36964
dc.description.abstract This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. the automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. the proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. the algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART. en
dc.format.extent 659-684
dc.language.iso eng
dc.publisher Mit Press
dc.relation.ispartof Evolutionary Computation
dc.rights Acesso restrito
dc.subject Decision trees en
dc.subject hyper-heuristics en
dc.subject automatic algorithm design en
dc.subject supervised machine learning en
dc.subject data mining en
dc.title Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms en
dc.type Carta
dc.contributor.institution Universidade de São Paulo (USP)
dc.contributor.institution Universidade Federal de São Paulo (UNIFESP)
dc.contributor.institution Univ Kent
dc.description.affiliation Univ São Paulo, Sao Carlos, SP, Brazil
dc.description.affiliation Universidade Federal de São Paulo, Sao Jose Dos Campos, Brazil
dc.description.affiliation Univ Kent, Canterbury, Kent, England
dc.description.affiliationUnifesp Universidade Federal de São Paulo, Sao Jose Dos Campos, Brazil
dc.identifier.doi 10.1162/EVCO_a_00101
dc.description.source Web of Science
dc.identifier.wos WOS:000326579700005



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