期刊名称：International Journal of Artificial Intelligence & Applications (IJAIA)
印刷版ISSN：0976-2191
电子版ISSN：0975-900X
出版年度：2019
卷号：10
期号：4
页码：1-8
DOI：10.5121/ijaia.2019.10401
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Data mining enables an innovative, largely automatic meta-analysis of the relationship between political and economic geography analyses of crisis regions. As an example, thetwo 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 sourcedata 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 TheFund for Peace in Washington, DC. In contrast to the quantitative GCRI, the FSI is essentiallyfocused on qualitative data from systematic interviews with experts. Both approaches therefore have closely related objectives, but very different methodologiesand 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 proposean 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..
