Sylvester Manlangit, S. Azam, Bharanidharan Shanmugam, Asif Karim
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引用次数: 11
Abstract
Fraudulent credit card transactions are on the rise and have become a significantly problematic issue for financial intuitions and individuals. Various methods have already been implemented to handle the issue, but the embezzlers have always managed to employ innovative tactics to circumvent a number of security measures and execute the fraudulent transactions. Thus, instead of a rule-based system, an intelligent and adaptable machine learning based algorithm should be an answer to tackle such sophisticated digital theft. The presented framework uses k-NN for classification and utilises Principal Component Analysis (PCA) for raw data transformation. Neighbours (anomalies in data) were created using Synthetic Minority Oversampling Technique (SMOTE) and a distance-based feature selection method was employed. The proposed process performed well by having a precision and F-Score of 98.32% and 97.44% respectively for k-NN and 100% and 98.24% respectively for Time subset when using the misclassified instances. This work also demonstrates a larger and clearer classification breakdown, which aids in achieving higher precision rate and improved recall rate. In a view to accomplish such high accuracy, the original datum was transformed using Principal Component Analysis (PCA), neighbours (anomalies in data) were created using Synthetic Minority Oversampling Technique (SMOTE) and a distance based feature selection method was employed. The proposed process performed well when using the misclassified instances in the test dataset used in the previous work, while demonstrating a larger and clearer classification breakdown. To cite this document: Sylvester Manlangit, Sami Azam, Bharanidharan Shanmugam, and Asif karim, "Novel Machine Learning Approach for Analyzing Anonymous Credit Card Fraud Patterns", International Journal of Electronic Commerce Studies, Vol.10, No.2, pp.175-202, 2019. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1732
期刊介绍:
The IJECS is a double-blind referred academic journal for all fields of Electronic Commerce. To serve as an international platform, the IJECS encourages manuscript submissions from authors all around the world. As a multi-discipline journal, The IJECS welcome both technology oriented and business oriented electronic commerce research articles. The purpose of the International Journal of Electronic Commerce Studies is to promote electronic commerce research and provide worldwide scholars a place to publish their innovative work in electronic commerce. To be published in the journal, the manuscript must make strong empirical, theoretical, or practical contributions and highlight the significance of the contributions to the electronic commerce field. Thus, preference is given to submissions that test, extend, or build strong theoretical frameworks for electronic commerce theory, electronic commerce system development, and electronic commerce practice. The journal is not tied to any particular national context; the geographic distribution of authors publishing in the journal came from countries around the world. Articles introducing cases of innovative applications in electronic commerce around the world are also published in the journal. The journal provides scholars opportunities to realize the electronic commerce research and development around the world. Articles in the International Journal of Electronic Commerce Studies will include, but are not limited to the following areas.