TY - CHAP
T1 - Comparison of Classifiers Models for Prediction of Intimate Partner Violence
AU - Guerrero, Ashly
AU - Gutiérrez Cárdenas, Juan
AU - Romero, Vilma
AU - Ayma, Víctor H.
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - 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.
AB - 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.
KW - Intimate partner violence
KW - Multinomial logistic regression
KW - Naïve Bayes
KW - Random forest
KW - SMOTE
KW - Support Vector Machine
UR - https://hdl.handle.net/20.500.12724/11925
UR - https://www.mendeley.com/catalogue/39f74e24-2561-36f4-a9ad-4dd6506cf0de/
U2 - 10.1007/978-3-030-63089-8_30
DO - 10.1007/978-3-030-63089-8_30
M3 - Capítulo
AN - SCOPUS:85096470447
SN - 9783030630881
T3 - Advances in Intelligent Systems and Computing
SP - 469
EP - 488
BT - Advances in Intelligent Systems and Computing
A2 - Arai, Kohei
A2 - Kapoor, Supriya
A2 - Bhatia, Rahul
PB - Springer Science and Business Media Deutschland GmbH
T2 - Future Technologies Conference, FTC 2020
Y2 - 5 November 2020 through 6 November 2020
ER -