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[01045] Advances on intimate partner femicide applying machine learning techniques and algorithms

  • Session Time & Room : 5D (Aug.25, 15:30-17:10) @E803
  • Type : Contributed Talk
  • Abstract : Intimate partner femicide (IPF) is female’s leading cause of violent death worldwide. The accuracy of existing risk assessment instruments for IPF developed by conventional statistics is not completely competitive. In this study, machine learning techniques and algorithms for classification were used to discriminate between lethal or non-lethal violence against women by intimate partners and to detect which variables discern them the most. The obtained evidenced-based knowledge could assist professionals in predicting and preventing lethality.
  • Classification : 68Txx
  • Author(s) :
    • Esperanza Garcia-Vergara (Universidad Loyola Andalucia)
    • Carlos Fresneda-Portillo (Universidad Loyola Andalucía (Spain))