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Método de inducción de reglas de clasificación oblicuas mediante un algoritmo evolutivo
dc.contributor.author | Álvarez Macías, José Luis | spa |
dc.contributor.author | Mata Vázquez, Jacinto | spa |
dc.contributor.author | Riquelme Santos, José Cristóbal | spa |
dc.date.accessioned | 2020-10-27T00:21:29Z | |
dc.date.available | 2020-10-27T00:21:29Z | |
dc.date.issued | 2002-06-01 | |
dc.identifier.issn | 2539-2115 | |
dc.identifier.issn | 1657-2831 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12749/9062 | |
dc.description.abstract | En este articulo presentamos un nuevo metodología denominado OBLIC, para inducción de reglas de clasificación oblicuas no jerárquicas a partir de un conjunto de datos etiquetados. La base del método es un algoritmo evolutivo con codificación real para los individuos y basado en la estrategia de Pittsburgh. Así, cada individuo esta compuesto por un conjunto de reglas de clasificación que dividen el espacio de búsqueda en regiones para cada una de las clases del conjunto de datos. La función de bondad determina la exactitud de cada individuo mediante la exploración de estas regiones. El modelo de clasificación es deducido a partir del mejor individuo obtenido durante el proceso evolutivo. Para analizar los resultados se ofrece una comparativa entre OBLIC, C4.5 y 0C1 sobre un conjunto de bases de datos del UCI Repositorio. | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | spa | spa |
dc.publisher | Universidad Autónoma de Bucaramanga UNAB | |
dc.relation | https://revistas.unab.edu.co/index.php/rcc/article/view/1105/1077 | |
dc.relation.uri | https://revistas.unab.edu.co/index.php/rcc/article/view/1105 | |
dc.rights | Derechos de autor 2002 Revista Colombiana de Computación | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/co/ | |
dc.source | Revista Colombiana de Computación; Vol. 3 Núm. 1 (2002): Revista Colombiana de Computación; 7-20 | |
dc.subject | Innovaciones tecnológicas | |
dc.subject | Ciencia de los computadores | |
dc.subject | Desarrollo de tecnología | |
dc.subject | Ingeniería de sistemas | |
dc.subject | Investigaciones | |
dc.subject | Tecnologías de la información y las comunicaciones | |
dc.subject | TIC´s | |
dc.title | Método de inducción de reglas de clasificación oblicuas mediante un algoritmo evolutivo | spa |
dc.title.translated | Oblique classification rule induction method using an evolutionary algorithm | eng |
dc.type.driver | info:eu-repo/semantics/article | |
dc.type.local | Artículo | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_7a1f | |
dc.subject.keywords | Technological innovations | eng |
dc.subject.keywords | Computer science | eng |
dc.subject.keywords | Technology development | eng |
dc.subject.keywords | Systems engineering | eng |
dc.subject.keywords | Investigations | eng |
dc.subject.keywords | Information and communication technologies | eng |
dc.subject.keywords | ICT's | eng |
dc.subject.keywords | Data mining | eng |
dc.subject.keywords | Supervised learning | eng |
dc.subject.keywords | Classification | eng |
dc.subject.keywords | Evolutionary algorithms | eng |
dc.identifier.instname | instname:Universidad Autónoma de Bucaramanga UNAB | spa |
dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
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dc.subject.lemb | Desarrollo tecnológico | spa |
dc.subject.lemb | Innovaciones tecnológicas | spa |
dc.subject.lemb | Ciencias de la computación | spa |
dc.subject.lemb | Tecnologías de la información y la comunicación | spa |
dc.subject.lemb | Investigación | spa |
dc.identifier.repourl | repourl:https://repository.unab.edu.co | |
dc.description.abstractenglish | In this article we present a new methodology called OBLIC, for the induction of non-hierarchical oblique classification rules from a set of labeled data. The basis of the method is an evolutionary algorithm with real coding for individuals and based on the Pittsburgh strategy. Thus, each individual is composed of a set of classification rules that divide the search space into regions for each of the classes in the data set. The goodness function determines the correctness of each individual by exploring these regions. The classification model is deduced from the best individual obtained during the evolutionary process. To analyze the results, a comparison between OBLIC, C4.5 and 0C1 is offered on a set of databases from the UCI Repository. | eng |
dc.subject.proposal | Minería de datos | spa |
dc.subject.proposal | Aprendizaje supervisado | spa |
dc.subject.proposal | Clasificación | spa |
dc.subject.proposal | Algoritmos evolutivos | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/CJournalArticle | |
dc.rights.creativecommons | Attribution-NonCommercial-ShareAlike 4.0 International | * |