Machine learning application in the ex-combatant demobilization process on the Colombian armed conflict

Abstract

This research explores the potential of supervised machine learning models to support the decision-making process in demobilizing ex-combatants in the peace process in Colombia. Recent works apply machine learning in analyzing crime and national security; however, there are no previous studies in the specific contexts of demobilization in an armed conflict. Therefore, the present paper makes a significant contribution by training and evaluating four machine learning models, using a database composed of 52,139 individuals and 21 variables. From the obtained results, it was possible to conclude that the XGBoost algorithm is the most suitable for predicting the future status of an ex-combatant. The XGBoost presented an AUC score of 0.964 in the cross-validation stage and an AUC of 0.952 in the test stage, evidencing the high reliability of the model.