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dc.contributor.author Cervantes Canales, Jair
dc.contributor.author García Lamont, Farid
dc.contributor.author LOPEZ CHAU, ASDRUBAL
dc.contributor.author Huang, De-Shuang
dc.creator Cervantes Canales, Jair; 101829
dc.creator García Lamont, Farid; 216477
dc.creator LOPEZ CHAU, ASDRUBAL; 100664
dc.creator Huang, De-Shuang;#0000-0002-6759-2691
dc.date.accessioned 2016-05-11T16:46:59Z
dc.date.available 2016-05-11T16:46:59Z
dc.date.issued 2014
dc.identifier.isbn 978-3-319-09332-1
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/20.500.11799/41189
dc.description.abstract Over the past few years, has been shown that generalization power of Support Vector Machines (SVM) falls dramatically on imbalanced data-sets. In this paper, we propose a new method to improve accuracy of SVM on imbalanced data-sets. To get this outcome, firstly, we used undersampling and SVM to obtain the initial SVs and a sketch of the hyperplane. These support vectors help to generate new artificial instances, which will take part as the initial population of a genetic algorithm. The genetic algorithm improves the population in artificial instances from one generation to another and eliminates instances that produce noise in the hyperplane. Finally, the generated and evolved data were included in the original data-set for minimizing the imbalance and improving the generalization ability of the SVM on skewed data-sets. es
dc.language.iso eng es
dc.publisher Springer es
dc.relation.ispartofseries 10.1007/978-3-319-09333-8_85;
dc.rights openAccess
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0
dc.subject Support Vector Machines es
dc.subject Hybrid es
dc.subject Imbalanced es
dc.subject.classification INGENIERÍA Y TECNOLOGÍA
dc.title A hybrid algorithm to improve the accuracy of support vector machines on skewed data-sets es
dc.type Capítulo de Libro
dc.provenance Científica
dc.road Verde
dc.ambito Internacional es
dc.audience students
dc.audience researchers
dc.type.conacyt bookPart
dc.identificator 7


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  • Título
  • A hybrid algorithm to improve the accuracy of support vector machines on skewed data-sets
  • Autor
  • Cervantes Canales, Jair
  • García Lamont, Farid
  • LOPEZ CHAU, ASDRUBAL
  • Huang, De-Shuang
  • Fecha de publicación
  • 2014
  • Editor
  • Springer
  • Tipo de documento
  • Capítulo de Libro
  • Palabras clave
  • Support Vector Machines
  • Hybrid
  • Imbalanced
  • Los documentos depositados en el Repositorio Institucional de la Universidad Autónoma del Estado de México se encuentran a disposición en Acceso Abierto bajo la licencia Creative Commons: Atribución-NoComercial-SinDerivar 4.0 Internacional (CC BY-NC-ND 4.0)

Mostrar el registro sencillo del objeto digital

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