文章基本信息

标题：Performance Comparison of Data Mining Algorithms for the Predictive Accuracy of Credit Card Defaulters
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作者：Dr. Maruf Pasha ; Meherwar Fatima ; Abdul Manan Dogar 等
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2017
卷号：17
期号：3
页码：178-183
出版社：International Journal of Computer Science and Network Security
摘要：The use of credit card for a secure balance transfer is a need of time. Fraudulent activities are also arising due to the fast growth of transactions. The motive of this research is to compare the predictive accuracy of customer��s default payments using different data mining techniques. Accuracy can be predicted in more compact form than just describing binary result classification of ��Credible�� or ��Not Credible�� in respect of risk management. Normally, ��defaulters�� actual chance of default is mysterious. Six data mining techniques (FLDA, Naïve Bayes, J48, Logistic Regression, MLP, and IBK) are applied to the data-set. The results of this research indicate that the neural network performs best to predict the default of credit card clients and shows the highest accuracy.
关键词：Data mining algorithms; Credit card defaulters; Performance of data mining; Predictive accuracy of credit card defaulters