1. DSpace-CRIS UAEMEX
  2. Interoperabilidad Repositorio Nacional
  3. Producción
Please use this identifier to cite or link to this item: http://ri.uaemex.mx/handle20.500.11799/41189
DC FieldValueLanguage
dc.creatorJair Cervantes Canales-
dc.creatorFarid García Lamont-
dc.creatorASDRUBAL LOPEZ CHAU-
dc.creatorDe-Shuang Huang-
dc.date2014-
dc.identifierhttp://hdl.handle.net/20.500.11799/41189-
dc.descriptionOver 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.-
dc.languageeng-
dc.publisherSpringer-
dc.relation10.1007/978-3-319-09333-8_85;-
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0-
dc.source0302-9743-
dc.source978-3-319-09332-1-
dc.subjectSupport Vector Machines-
dc.subjectHybrid-
dc.subjectImbalanced-
dc.subjectinfo:eu-repo/classification/cti/7-
dc.titleA hybrid algorithm to improve the accuracy of support vector machines on skewed data-sets-
dc.typebookPart-
dc.audiencestudents-
dc.audienceresearchers-
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:Producción
Show simple item record

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.