Fadaei Noghani, F., Moattar, M. (2017). Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection. Journal of AI and Data Mining, 5(2), 235-243. doi: 10.22044/jadm.2016.788
F. Fadaei Noghani; M. Moattar. "Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection". Journal of AI and Data Mining, 5, 2, 2017, 235-243. doi: 10.22044/jadm.2016.788
Fadaei Noghani, F., Moattar, M. (2017). 'Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection', Journal of AI and Data Mining, 5(2), pp. 235-243. doi: 10.22044/jadm.2016.788
Fadaei Noghani, F., Moattar, M. Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection. Journal of AI and Data Mining, 2017; 5(2): 235-243. doi: 10.22044/jadm.2016.788
Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
Abstract
Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effective features, using an extended wrapper method, ensemble classification is performed. The extended feature selection approach includes a prior feature filtering and a wrapper approach using C4.5 decision tree. Ensemble classification, using cost sensitive decision trees is performed in a decision forest framework. A locally gathered fraud detection dataset is used to estimate the proposed method. The proposed method is assessed using accuracy, recall, and F-measure as evaluation metrics and compared with basic classification algorithms including ID3, J48, Naïve Bayes, Bayesian Network and NB tree. Experiments show that considering the F-measure as evaluation metric, the proposed approach yields 1.8 to 2.4 percent performance improvement compared to other classifiers.
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