Data-Driven State Fragility Index Measurement Through Classification Methods

FRONTIERS IN PHYSICS(2022)

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Abstract
As environmental changes cause a series of complex issues and unstable situation, exploring the impact of environmental changes is essential for national stability, which is helpful for early warning and provides guidance solutions for a country. The existing mainstream metric of national stability is the Fragile States Index, which includes many indicators such as abstract concepts and qualitative indicators by experts. In addition, these indicators may have preferences and bias because some data sources come from unreliable platforms; it may not reflect the real situation for the current status of countries. In this article, we propose a method based on ensemble learning, named CR, which can be obtained by quantifiable indicators to reflect national stability. Compared with the current mainstream methods, our proposed CR method highlights quantitative factors and reduces qualitative factors, which is an advantage of simplicity and interoperability. The extensive experimental results show a significant improvement over the SOTA methods (7.13% improvement in accuracy, 2.02% improvement in correlation).
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Key words
data-driven, quantifiable, ranking, state for fragility, classification
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