DocumentCode :
131516
Title :
Applying game theory rules to enhance decision support systems in credit and financial applications
Author :
Alskheliwi, Turki ; Jim, Carol ; Lateef, Khalid ; Penn, Stephen ; Salem, Ashraf
Author_Institution :
Dept. of Comput. Sci. & Inf. Technol., Hood Coll., Frederick, MD, USA
fYear :
2014
fDate :
28-30 July 2014
Firstpage :
1
Lastpage :
10
Abstract :
This paper 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 use the German credit dataset using a variety of different data mining mechanisms then we propose a combined model to enhance the results using Game Theory principles and the decision tree “J48” algorithm as a data mining mechanism.
Keywords :
data mining; decision support systems; decision trees; financial data processing; game theory; risk management; German credit dataset; J48 algorithm; credit application; data mining mechanism; decision support systems; decision tree algorithm; financial application; game theory; risk prediction; Accuracy; Data mining; Game theory; Games; Genetic programming; Neural networks; Support vector machines; Data mining; Game Theory; Machine Learning; WEKA; data classification; decision trees; genetic algorithm; genetic programming; neural networks; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Games: AI, Animation, Mobile, Multimedia, Educational and Serious Games (CGAMES), 2014
Conference_Location :
Louisville, KY
Print_ISBN :
978-1-4799-5853-5
Type :
conf
DOI :
10.1109/CGames.2014.6934138
Filename :
6934138
Link To Document :
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