dc.creator | Tallón Ballesteros, Antonio Javier | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2016-06-24T08:21:51Z | |
dc.date.available | 2016-06-24T08:21:51Z | |
dc.date.issued | 2014 | |
dc.identifier.isbn | 978-3-319-10839-1 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | http://hdl.handle.net/11441/42721 | |
dc.description.abstract | This paper introduces the use of an ant colony optimization
(ACO) algorithm, called Ant System, as a search method in two wellknown
feature subset selection methods based on correlation or consistency
measures such as CFS (Correlation-based Feature Selection) and
CNS (Consistency-based Feature Selection). ACO guides the search using
a heuristic evaluator. Empirical results on twelve real-world classification
problems are reported. Statistical tests have revealed that InfoGain is a
very suitable heuristic for CFS or CNS feature subset selection methods
with ACO acting as search method. The use of InfoGain is shown to be
the significantly better heuristic over a range of classifiers. The results
achieved by means of ACO-based feature subset selection with the suitable
heuristic evaluator are better for most of the problems comparing
with those obtained with CFS or CNS combined with Best First search. | es |
dc.description.sponsorship | MICYT TIN2007-68084- C02-02 | |
dc.description.sponsorship | MICYT TIN2011-28956-C02-02 | |
dc.description.sponsorship | Junta de Andalucía P11-TIC-7528 | |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Intelligent Data Engineering and Automated Learning – IDEAL 2014: 15th International Conference, Salamanca, Spain, September 10-12, 2014. Proceedings. Lecture Notes in Computer Science, v.8669 | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Feature selection | es |
dc.subject | classification | es |
dc.subject | ant colony optimization | es |
dc.subject | heuristic evaluator | es |
dc.subject | filter | es |
dc.subject | feature subset selection | es |
dc.title | Tackling Ant Colony Optimization Meta-Heuristic as Search Method in Feature Subset Selection Based on Correlation or Consistency Measures | es |
dc.type | info:eu-repo/semantics/bookPart | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2007-68084- C02-02 | es |
dc.relation.projectID | TIN2011-28956-C02-02 | es |
dc.relation.projectID | P11-TIC-7528 | es |
dc.identifier.doi | http://dx.doi.org/10.1007/978-3-319-10840-7_47 | es |
idus.format.extent | 8 | es |
dc.publication.initialPage | 386 | es |
dc.publication.endPage | 393 | es |
dc.relation.publicationplace | Switzerland | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/42721 | |