Classification of fragile states based on machine learning

Abstract

The study of fragile states has become a significant issue in global security, development and poverty at present. The existing classification methods of fragile state, which is a simple addition to the national index and threshold segmentation, is not reasonable enough. We introduce a new method based on machine learning. With this method, it will be easier and more reasonable to classify a country. We use two kinds of classifier, one of which is the support vector machine, and the other is the gradient boosted regression trees. Both models have flaws, so we use ensemble learning techniques to combine them. First of all, subjective labelling of a part of the national data to allows the machine to learn why a country becomes vulnerable from these data, and how to classify the vulnerability class of a country. Then, we trained the model with the data, and divided fragile states into four categories successfully (Alert, Warning, Stable and Sustainable). For the classification result, our model got a 93% test error rate, and a 96% training error rate, which is better than 77% with the threshold segmentation method.