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dc.contributor.authorGuerrero, Ashly
dc.contributor.authorGutiérrez Cárdenas, Juan Manuel
dc.contributor.authorRomero Romero, Vilma Susana
dc.contributor.authorAyma Quirita, Victor Hugo
dc.contributor.otherGuerrero, Ashly
dc.contributor.otherGutiérrez Cárdenas, Juan Manuel
dc.contributor.otherRomero Romero, Vilma Susana
dc.contributor.otherAyma Quirita, Víctor Hugo
dc.date.accessioned2020-12-01T16:17:47Z
dc.date.available2020-12-01T16:17:47Z
dc.date.issued2021
dc.identifier.citationGuerrero, A., Cárdenas, J. G., Romero, V. & Ayma, V. H. (2021) Comparison of Classifiers Models for Prediction of Intimate Partner Violence. In: Arai K., Kapoor S., Bhatia R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, (2), 469-488. Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978-3-030-63089-8_30es_PE
dc.identifier.issn2194-5357
dc.identifier.urihttps://hdl.handle.net/20.500.12724/11925
dc.descriptionIndexado en Scopuses_PE
dc.description.abstractIntimate partner violence (IPV) is a problem that has been studied by different researchers to determine the factors that influence its occurrence, as well as to predict it. In Peru, 68.2% of women have been victims of violence, of which 31.7% were victims of physical aggression, 64.2% of psychological aggression, and 6.6% of sexual aggression. Therefore, in order to predict psychological, physical and sexual intimate partner violence in Peru, the database of denouncements registered in 2016 of the “Ministerio de la Mujer y Poblaciones Vulnerables” was used. This database is comprised of 70510 complaints and 236 variables concerning the characteristics of the victim and the aggressor. First of all, we used Chi-squared feature selection technique to find the most influential variables. Next, we applied the SMOTE and random under sampling techniques to balance the dataset. Then, we processed the balanced dataset using cross validation with 10 folds on Multinomial Logistic Regression, Random Forest, Naive Bayes and Support Vector Machines classifiers to predict the type of partner violence and compare their results. The results indicate that the Multinomial Logistic Regression and Support Vector Machine classifiers performed better on different scenarios with different feature subsets, whereas the Naïve Bayes classifier showed inferior. Finally, we observed that the classifiers improve their performance as the number of features increased.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherSpringeres_PE
dc.relation.ispartofurn:issn:2194-5357
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceRepositorio Institucional - Ulimaes_PE
dc.sourceUniversidad de Limaes_PE
dc.subjectProspectivaes_PE
dc.subjectViolencia de géneroes_PE
dc.subjectForecastinges_PE
dc.subjectGender-based violencees_PE
dc.subject.classificationIngeniería de sistemas / Diseño y métodoses_PE
dc.titleComparison of Classifiers Models for Prediction of Intimate Partner Violencees_PE
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.type.otherArtículo de conferencia en Scopuses_PE
ulima.areas.lineasdeinvestigacionProductividad y empleo / Innovación: tecnologías y productoses_PE
ulima.areas.lineasdeinvestigacionDerechos, Estado y democracia / Géneroes_PE
dc.identifier.journalAdvances in Intelligent Systems and Computinges_PE
dc.publisher.countryCHes_PE
dc.description.peer-reviewRevisión por pareses_PE
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#2.02.04
dc.identifier.doihttps://doi.org/10.1007/978-3-030-63089-8_30
ulima.autor.afiliacionGuerrero, Ashly (Universidad de Lima) (Scopus)es_PE
ulima.autor.afiliacionGutiérrez Cárdenas, Juan Manuel (Universidad de Lima) (Scopus)es_PE
ulima.autor.afiliacionRomero Romero, Vilma Susana (Universidad de Lima) (Scopus)es_PE
ulima.autor.carreraGuerrero, Ashly (No figura en la lista del año 2020)es_PE
ulima.autor.carreraGutiérrez Cárdenas, Juan Manuel (Ingeniería de Sistemas)es_PE
ulima.autor.carreraRomero Romero, Vilma Susana (Ingeniería de Sistemas)es_PE


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