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OFAI-TR-94-33 ( 91kB g-zipped PostScript file)

Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome

Johannes Fürnkranz, Johann Petrak, Robert Trappl, Jacob Bercovitch

This paper tries to identify rules and factors that are predictive for the outcome of international conflict management attempts. We use C4.5, an advanced Machine Learning algorithm, for generating decision trees and prediction rules from cases in the CONFMAN database. The results show that simple patterns and rules are often not only more understandable, but also more reliable than complex rules. Simple decision trees are able to improve the chances of correctly predicting the outcome of a conflict management attempt. This suggests that mediation is more repetitive than conflicts per se, where such results have not been achieved so far.

Citation: Fürnkranz J., Petrak J., Trappl R., Bercovitch J.: Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome , Austrian Research Institute for Artificial Intelligence, Vienna, TR-94-33, 1994.