Abstract
Data mining enables an innovative, largely automatic meta-analysis of the relationship between political and economic geography analyses of crisis regions. As an example, the two approaches Global Conflict Risk Index (GCRI) and Fragile States Index (FSI) can be related to each other. The GCRI is a quantitative conflict risk assessment based on open source data and a statistical regression method developed by the Joint Research Centre of the European Commission. The FSI is based on a conflict assessment framework developed by The Fund for Peace in Washington, DC. In contrast to the quantitative GCRI, the FSI is essentially focused on qualitative data from systematic interviews with experts. Both approaches therefore have closely related objectives, but very different methodologies and data sources. It is therefore hoped that the two complementary approaches can be combined to form an even more meaningful meta-analysis, or that contradictions can be discovered, or that a validation of the approaches can be obtained if there are similarities. We propose an approach to automatic meta-analysis that makes use of machine learning (data mining). Such a procedure represents a novel approach in the meta-analysis of conflict risk analyses.
Highlights
Data mining enables an innovative, largely automatic meta-analysis of the relationship between political and economic geography analyses of crisis regions
The Global Conflict Risk Index (GCRI) is a quantitative conflict risk assessment based on open source data and a statistical regression method developed by the Joint Research Centre of the European Commission
The Fragile States Index (FSI) is based on a conflict assessment framework developed by The Fund for Peace in Washington, DC
Summary
Data mining enables an innovative, largely automatic meta-analysis of the relationship between political and economic geography analyses of crisis regions. We propose an approach to automatic meta- analysis that makes use of machine learning (data mining). Such a procedure represents a novel approach in the meta-analysis of conflict risk analyses. Backpropagation belongs to the group of supervised learning methods and is applied to multilayered perceptrons in that an external teaching function knows the desired output, the target value, for a sample of inputs. This sample is known as a training set. We briefly summarize the mathematical properties of this robust and proven data mining approach
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have