Abstract
The recent technology enhancement in the mobile and e-commerce field includes a huge number of online transaction, which results an increased number of fraud transaction and made a notable financial loss, for the individuals and banking sectors. Significant number of fraud transactions are made with credit cards. So, it is essential to develop a mechanism that ensures security and integrity of credit card transactions. In this article the main aim is to detect such fraud transactions using several machine learning algorithms such as Decision Tree & Adaptive Boosting. Due to high imbalance in dataset, the Synthetic Minority Oversampling Technique (SMOTE) is used to balance the data and Decision tree algorithm for classification. The Decision tree algorithm is used with the Adaptive Boosting technique to increase their quality of binary classification. The results are compared using the accuracy, precision and recall.
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Prasanna Kumar, K.R., Aravind, S., Gopinath, K., Navienkumar, P., Logeswaran, K., Gunasekar, M. (2023). Enhancing the Credit Card Fraud Detection Using Decision Tree and Adaptive Boosting Techniques. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_36
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DOI: https://doi.org/10.1007/978-3-031-35501-1_36
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