Country Corruption Analysis with Self Organizing Maps and Support Vector Machines

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...

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Hauptverfasser: Huysmans, Johan, Martens, David, Baesens, Bart, Vanthienen, Jan, Van Gestel, Tony
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Martens, David
Baesens, Bart
Vanthienen, Jan
Van Gestel, Tony
description 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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source Springer Books
subjects Applied sciences
Best Match Unit
Civil Liberty
Component Plane
Computer science
control theory
systems
Corruption Perception Index
Data processing. List processing. Character string processing
Exact sciences and technology
Information systems. Data bases
Memory and file management (including protection and security)
Memory organisation. Data processing
Software
Support Vector Machine
title Country Corruption Analysis with Self Organizing Maps and Support Vector Machines
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