Predicting Armed Conflict Probability: A Multi-Factor Machine Learning Approach
Ivchenko, Oleh;
Predicting Armed Conflict Probability: A Multi-Factor Machine Learning Approach
Machine learning ensemble approach to predicting armed conflict using ACLED, UCDP, World Bank, SIPRI, and V-Dem data. Achieves 87.3% accuracy with XGBoost, Random Forest, and LSTM models.
ACLED, machine learning, conflict prediction, political violence, ensemble methods, early warning systems, war prediction, LSTM, armed conflict, XGBoost
ACLED, machine learning, conflict prediction, political violence, ensemble methods, early warning systems, war prediction, LSTM, armed conflict, XGBoost
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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