suggested in (Guyon and Elisseeff, 2003). The rankings of the effects of variables on the MID for NN and SVM vary as shown in table III. The reason for the variance may be because in the case of

| Rank   | NN          | SVM         |
|--------|-------------|-------------|
| 1      | Democracy   | Contiguity  |
| 2      | Capability  | Distance    |
| 3      | Contiguity  | Major power |
| 4      | Distance    | Capability  |
| S      | Alliance    | Democracy   |
| 6      | Dependency  | Dependency  |
| 7      | Major power | Alliance    |

## V. Conclusion

In this paper two artificial intelligence techniques, neural networks (NN) and support vector machine (SVM), are used to predict militarised interstate disputes. The independent/input variables are Democracy, Allies, Contingency, Distance, Capability, Dependency and Major Power while the dependent/ output variable is MID result which is either peace or conflict. A neural network trained with scaled conjugate gradient algorithm and an SVM with a radial basis kernel function together with grid-search and cross-validation techniques were employed to find the optimal model.