Country corruption analysis with self organizing maps and support vector machines

Huysmans, J., Martens, D., Baesens, B., Vanthienen, J. and Van Gestel, T. (2006) Country corruption analysis with self organizing maps and support vector machines. Chen, Hsinchun, Wang, Fei-Yue, Yang, Christopher C., Zeng, Daniel Dajun, Chau, Michael and Chang, Kuiyu (eds.) In Proceedings of the Tenth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006), Workshop on Intelligence and Security Informatics (WISI), Lecture Notes in Computer Science (LNCS). Springer. pp. 103-114 . (doi:10.1007/11734628_13).

Abstract

During recent years, the empirical research on corruption has grown considerably. Possible links between government corruption and terrorism have attracted an increasing interest in this research field. Most of the existing literature discusses the topic from a socio-economical perspective and only few studies tackle this research field from a data mining point of view. In this paper, we apply data mining techniques onto a cross-country database linking macro-economical variables to perceived levels of corruption. In the first part, self organizing maps are applied to study the interconnections between these variables. Afterwards, support vector machines are trained on part of the data and used to forecast corruption for other countries. Large deviations for specific countries between these models’ predictions and the actual values can prove useful for further research. Finally, projection of the forecasts onto a self organizing map allows a detailed comparison between the different models’ behavior.

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Contributors

Author: B. Baesens

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