Applying Game Theoretic Techniques to Improve the Accuracy of Tree-based Classification Results

The application of game-theoretic techniques to enhance data mining results in financial applications has been widely explored. While results have been promising, further investigation is needed to generate a more robust model and minimize errors. In this work, a two-step, data mining and game-theoretic analysis model is examined to reduce classification error and improve predictions. Using credit-worthiness and loan applications from the German Credit dataset, we are able to reduce classification errors using payoff tables, game trees, and associated binomial distributions. Our results show that applying game-theoretic techniques after data mining results in a combined model can improve overall accuracy and enhance decision accuracy.