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Article

Modeling and Forecasting Gender-Based Violence through Machine Learning Techniques

1
Departamento de Ingeniería de Comunicaciones, ATIC Research Group, Universidad de Málaga, 29071 Málaga, Spain
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Instituto Universitario de Investigación de Estudios de Género, Universitat d’Alacant, 03080 Alicante, Spain
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Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
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Departamento de Ciencias Sociales y Humanas, Universidad Miguel Hernández de Elche, 03202 Elche, Spain
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Dipartimento di Ingegneria Informatica Automatica e Gestionale ‘Antonio Ruberti’, Sapienza Università di Roma, 00185 Roma, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(22), 8244; https://doi.org/10.3390/app10228244
Received: 28 October 2020 / Revised: 13 November 2020 / Accepted: 18 November 2020 / Published: 20 November 2020
Gender-Based Violence (GBV) is a serious problem that societies and governments must address using all applicable resources. This requires adequate planning in order to optimize both resources and budget, which demands a thorough understanding of the magnitude of the problem, as well as analysis of its past impact in order to infer future incidence. On the other hand, for years, the rise of Machine Learning techniques and Big Data has led different countries to collect information on both GBV and other general social variables that in one way or another can affect violence levels. In this work, in order to forecast GBV, firstly, a database of features related to more than a decade’s worth of GBV is compiled and prepared from official sources available due to Spain’s open access. Then, secondly, a methodology is proposed that involves testing different methods of features selection so that, with each of the subsets generated, four techniques of predictive algorithms are applied and compared. The tests conducted indicate that it is possible to predict the number of GBV complaints presented to a court at a predictive horizon of six months with an accuracy (Root Median Squared Error) of 0.1686 complaints to the courts per 10,000 inhabitants—throughout the whole Spanish territory—with a Multi-Objective Evolutionary Search Strategy for the selection of variables, and with Random Forest as the predictive algorithm. The proposed methodology has also been successfully applied to three specific Spanish territories of different populations (large, medium, and small), pointing to the presented method’s possible use elsewhere in the world. View Full-Text
Keywords: gender-based violence; machine learning; information and communication technologies; multi-objective evolutionary search; random forest; time series forecasting gender-based violence; machine learning; information and communication technologies; multi-objective evolutionary search; random forest; time series forecasting
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MDPI and ACS Style

Rodríguez-Rodríguez, I.; Rodríguez, J.-V.; Pardo-Quiles, D.-J.; Heras-González, P.; Chatzigiannakis, I. Modeling and Forecasting Gender-Based Violence through Machine Learning Techniques. Appl. Sci. 2020, 10, 8244. https://doi.org/10.3390/app10228244

AMA Style

Rodríguez-Rodríguez I, Rodríguez J-V, Pardo-Quiles D-J, Heras-González P, Chatzigiannakis I. Modeling and Forecasting Gender-Based Violence through Machine Learning Techniques. Applied Sciences. 2020; 10(22):8244. https://doi.org/10.3390/app10228244

Chicago/Turabian Style

Rodríguez-Rodríguez, Ignacio, José-Víctor Rodríguez, Domingo-Javier Pardo-Quiles, Purificación Heras-González, and Ioannis Chatzigiannakis. 2020. "Modeling and Forecasting Gender-Based Violence through Machine Learning Techniques" Applied Sciences 10, no. 22: 8244. https://doi.org/10.3390/app10228244

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