TY - GEN
T1 - Artificial intelligence for conflict management
AU - Habtemariam, E.
AU - Marwala, T.
AU - Lagazio, M.
PY - 2005
Y1 - 2005
N2 - Militarised conflict is one of the risks that have a significant impact on society. Militarised Interstate Dispute (MID) is defined as an outcome of interstate interactions which result on either peace or conflict. Effective prediction of the possibility of conflict between states is an important decision support tool for policy makers. In a previous research, neural networks (NNs) have been implemented to predict the MID. Support Vector Machines (SVMs) have proven themselves to be very good prediction techniques and are introduced for the prediction of MIDs in this study. The results found show that SVM predicts MID better than NN while NN gives more consistent and easy to interpret sensitivity analysis results than SVM.
AB - Militarised conflict is one of the risks that have a significant impact on society. Militarised Interstate Dispute (MID) is defined as an outcome of interstate interactions which result on either peace or conflict. Effective prediction of the possibility of conflict between states is an important decision support tool for policy makers. In a previous research, neural networks (NNs) have been implemented to predict the MID. Support Vector Machines (SVMs) have proven themselves to be very good prediction techniques and are introduced for the prediction of MIDs in this study. The results found show that SVM predicts MID better than NN while NN gives more consistent and easy to interpret sensitivity analysis results than SVM.
UR - http://www.scopus.com/inward/record.url?scp=33750143407&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2005.1556310
DO - 10.1109/IJCNN.2005.1556310
M3 - Conference contribution
AN - SCOPUS:33750143407
SN - 0780390482
SN - 9780780390485
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2583
EP - 2588
BT - Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
T2 - International Joint Conference on Neural Networks, IJCNN 2005
Y2 - 31 July 2005 through 4 August 2005
ER -