ENHANCING RISK PREDICTION IN FINANCIAL APPLICATIONS USING DATA MINING AND GAME THEORY PRINCIPLES
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Date
2016-05
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Hood College Information Technology
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Hood College Information Technology
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Abstract
This thesis examines the potential of applying Game Theory to Data Mining
mechanisms to enhance the accuracy of predicting risk in .financial settings. There have
been many attempts made in the past to enhance Data Mining results using different
methods including Game Theory principles. Despite the promising results of previous
work in integrating Game Theory and Data Mining, further research is needed to explore
the potential of creating a combined model that can be applied to a range of datasets to
successfully enhance risk prediction. We apply a variety of different tree data mining
algorithms to the German Credit Dataset. Then, we propose a combined model to
enhance the accuracy of the data mining results by using Game Theory principles. Our
approach focuses on correcting the error from the incorrectly classified instances by our
proposed enhanced game tree model. By using the payoff table derived from our
enhanced game tree model and the binomial distribution, we can determine the
percentage of enhancement to the tree-based data mining results. Our results show that
applying Game Theory principles to Data Mining techniques in a combined model can
improve overall accuracy and enhance decision support systems in financial applications.