Comparison of Classifiers Models for Prediction of Intimate Partner Violence

Ashly Guerrero, Juan Gutiérrez Cárdenas, Vilma Romero, Víctor H. Ayma

Producción científica: Capítulo del libro/informe/acta de congresoCapítulo

4 Citas (Scopus)


Intimate 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.

Idioma originalInglés
Título de la publicación alojadaAdvances in Intelligent Systems and Computing
EditoresKohei Arai, Supriya Kapoor, Rahul Bhatia
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas20
ISBN (versión impresa)9783030630881
EstadoPublicada - 1 nov. 2020
EventoFuture Technologies Conference, FTC 2020 - San Francisco, Estados Unidos
Duración: 5 nov. 20206 nov. 2020

Serie de la publicación

NombreAdvances in Intelligent Systems and Computing


ConferenciaFuture Technologies Conference, FTC 2020
País/TerritorioEstados Unidos
CiudadSan Francisco


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