Comparison of Classifiers Models for Prediction of Intimate Partner Violence

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

Research output: Chapter in Book/Report/Conference proceedingPaper (Conference contribution)peer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publicationProceedings of the Future Technologies Conference, FTC 2020, Volume 2
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages20
ISBN (Print)9783030630881
StatePublished - 2021
EventFuture Technologies Conference, FTC 2020 - San Francisco, United States
Duration: 5 Nov 20206 Nov 2020

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceFuture Technologies Conference, FTC 2020
Country/TerritoryUnited States
CitySan Francisco


  • Intimate partner violence
  • Multinomial logistic regression
  • Naïve Bayes
  • Random forest
  • Support Vector Machine


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