militarised conflict. The unit of analysis is a dyad-year. After the omission, a total of 27737 with 26845 peace and 892 conflict dyad-years were filtered out. 

The dyadic data was classified into two sets, which are training and testing set. In their study 
(Lagazio and Russett, 2003), have given a detailed discussion on how the training set should be chosen. They have found out that a balanced set, equal number of conflict and peace dyads, gives best results as training set for the neural network. The same principle was adhered to in this study. The training set contains 1000 randomly chosen dyads, 500 from each group. The test set contains 26737 dyads of which 392 are conflict and 26345 non-conflict dyads. 

## Iv. Results And Discussion

Neural network and support vector machine were employed to classify the MID data. The main focus of the result is to look at the percentage of correct MID prediction of the test data set by each technique. Table I depicts the confusion matrix of the results. Although NN performed as good as SVM in predicting true conflicts (true positives), this is achieved at the expense of reducing the number of correct peace (true negatives) prediction. SVM picked up the true conflicts (true positives) better than NN without effectively minimising the number of true peace (true negatives). SVM is able to pick 1450 more cases of the true peace (true negatives) than NN, which makes it a better choice than NN. Over all, SVM is able to predict peace and conflict with 79% and 75%, respectively. The corresponding results for NN are 74% and 76%, respectively for peace and conflict. The combined results of correct predictions are 79% for SVM and 74% for NN. 