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How to cite this paper
APA: Rawashdeh, E., Al-Ramahi, N., Ahmad, H & Zaghloul, R. (2024). Efficient credit card fraud detection using evolutionary hybrid feature selection and random weight networks. International Journal of Data and Network Science, 8(1), 463-472.
Chicago/Turabian: Rawashdeh, E., Al-Ramahi, N., Ahmad, H & Zaghloul, R. 2024. "Efficient credit card fraud detection using evolutionary hybrid feature selection and random weight networks." International Journal of Data and Network Science 8, no. 1 (2024): 463-472.
AMA: Rawashdeh, E., Al-Ramahi, N., Ahmad, H & Zaghloul, R. Efficient credit card fraud detection using evolutionary hybrid feature selection and random weight networks. International Journal of Data and Network Science. 2024;8(1):463-472.
References
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Abdel-Basset, M., Abdel-Fatah, L., & Sangaiah, A. K. (2018). Metaheuristic algorithms: A comprehensive review. Computa-tional intelligence for multimedia big data on the cloud with engineering applications, 185-231.
Ahmad, H., Kasasbeh, B., Aldabaybah, B., & Rawashdeh, E. (2023). Class balancing framework for credit card fraud detec-tion based on clustering and similarity-based selection (SBS). International Journal of Information Technology, 15(1), 325-333.
Ahmad, H., Kasasbeh, B., AL-Dabaybah, B., & Rawashdeh, E. (2023). EFN-SMOTE: An effective oversampling technique for credit card fraud detection by utilizing noise filtering and fuzzy c-means clustering. International Journal of Data and Network Science, 7(3), 1025-1032.
Bin Sulaiman, R., Schetinin, V., & Sant, P. (2022). Review of machine learning approach on credit card fraud detection. Hu-man-Centric Intelligent Systems, 2(1-2), 55-68.
Biswas, M., & Debbarma, S. (2022, June). An Efficient Approach for Credit Card Fraud Identification with the Oversampling Method. In International Conference on Frontiers of Intelligent Computing: Theory and Applications (pp. 273-286). Singa-pore: Springer Nature Singapore.
Cao, W., Wang, X., Ming, Z., & Gao, J. (2018). A review on neural networks with random weights. Neurocomputing, 275, 278-287.
Cheng, R., & Jin, Y. (2015). A Competitive Swarm Optimizer for Large Scale Optimization. IEEE Transactions on Cybernet-ics, 45(2), 191 - 204.
Cherif, A., Badhib, A., Ammar, H., Alshehri , S., Kalkatawi, M., & Imine, A. (2023). Credit card fraud detection in the era of disruptive technologies: A systematic review. Journal of King Saud University - Computer and Information Sciences, 35(1), 145-174.
Esenogho, E., Mienye, I. D., Swart, T. G., Aruleba, K., & Obaido, G. (2022). A Neural Network Ensemble With Feature Engi-neering for Improved Credit Card Fraud Detection. IEEE Access, 10, 16400 - 16407.
Habibi, A., Delavar, M. R., Sadeghian, M. S., Nazari, B., & Pirasteh, S. (2023). A hybrid of ensemble machine learning models with RFE and Boruta wrapper-based algorithms for flash flood susceptibility assessment. International Journal of Applied Earth Observation and Geoinformation, 122, 103401.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004, July). Extreme learning machine: a new learning scheme of feedforward neural networks. In 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541) (Vol. 2, pp. 985-990). Ieee.
Ileberi, E., Sun, Y., & Wang, Z. (2022). A machine learning based credit card fraud detection using the GA algorithm for fea-ture selection. Journal of Big Data, 9(1), 1-17.
Jiao, R., Nguyen, B. H., Xue, B., & Zhang, M. (2023). A Survey on Evolutionary Multiobjective Feature Selection in Classifi-cation: Approaches, Applications, and Challenges. IEEE Transactions on Evolutionary Computation.
Jovanovic, D., Antonijevic, M., Stankovic, M., Zivkovic, M., Tanaskovic, M., & Bacanin, N. (2022). Tuning machine learning models using a group search firefly algorithm for credit card fraud detection. Mathematics, 10(13), 2272.
Karthika, J., & Senthilselvi, A. (2022). Credit Card Fraud Detection based on Ensemble Machine Learning Classifiers. 3rd In-ternational Conference on Electronics and Sustainable Communication Systems (ICESC). Coimbatore: IEEE.
Kaur, B. J., & Kumar, R. (2020). A hybrid approach for credit card fraud detection using naive Bayes and voting classifier. In Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI-2019) (pp. 731-740). Spring-er International Publishing.
Kohavi, R., & John, G. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97.
Lima, R. F., & Pereira, A. (2017). Feature Selection Approaches to Fraud Detection in e-Payment Systems. International Con-ference on Electronic Commerce and Web Technologies (pp. 111-126). Springer.
Liu, H., Zhou, M., & Liu, Q. (2019). An embedded feature selection method for imbalanced data classification. IEEE/CAA Journal of Automatica Sinica, 6(3), 703-715.
Liu, O., Ma, J., Poon, P.-L., & Zhang, J. (2009). On an Ant Colony-Based Approach for Business Fraud Detection. Interna-tional Conference on Intelligent Computing. 5754, pp. 1104–1111. Springer.
Majhi, S. K. (2021). Fuzzy clustering algorithm based on modified whale optimization algorithm for automobile insurance fraud detection. Evolutionary Intelligence, 14, 35-46.
Malik, E. F., Khaw, K. W., Belaton, B., Wong, W. P., & Chew, X. (2022). Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture. Mathematics, 10(9).
Masoud, M., Jaradat, Y., Rababa, E., & Manasrah, A. (2021). Turnover Prediction using Machine Learning: Empirical Study. International Journal of Advances in Soft Computing & Its Applications, 13(1).
Mienye, I. D., & Sun, Y. (2023). A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection. Applied Sciences, 13(12), 7254.
Prasetiyowati, M. I., Maulidevi, N. U., & Surendro, K. (2021). Determining threshold value on information gain feature selec-tion to increase speed and prediction accuracy of random forest. Journal of Big Data, 8(1), 84.
Rakesh, D. K., & Jana, P. (2023). An improved differential evolution algorithm for quantifying fraudulent transactions. Pat-tern Recognition, 141.
Rawashdeh, E., Aljarah , I., & Faris, H. (2021). A cooperative coevolutionary method for optimizing random weight networks and its application for medical classification problems. Journal of Ambient Intelligence and Humanized Computing, 12, 321–342.
Roseline, J. F., Naidu, G. B. S. R., Pandi, V. S., alias Rajasree, S. A., & Mageswari, N. (2022). Autonomous credit card fraud detection using machine learning approach☆. Computers and Electrical Engineering, 102, 108132.
Rtayli, N., & Enneya, N. (2020). Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization. Journal of Information Security and Applications, 55.
Schmidt, W. F., Kraaijveld, M. A., & Duin, R. P. (1992). Feedforward neural networks with random weights. Conference B: Pattern Recognition Methodology and Systems (pp. 1-4). IEEE.
Shenvi, P., Samant, N., Kumar, S., & Kulkarni, V. (2019). Credit Card Fraud Detection using Deep Learning. IEEE 5th Inter-national Conference for Convergence in Technology (I2CT). Bombay: IEEE.
Shirgave, S., Awati, C., More, R., & Patil, S. (2019). A Review On Credit Card Fraud Detection Using Machine Learning. In-ternational Journal Of Scientific & Technology Research, 8(10).
Singh, A., & Jain, A. (2020). Cost-sensitive metaheuristic technique for credit card fraud detection. Journal of Information and Optimization Sciences, 41(6), 1319-1331.
Song, Q., Jiang, H., & Liu, J. (2017). Feature selection based on FDA and F-score for multi-class classification. Expert Systems with Applications, 81, 22-27.
Thai-Nghe, N., Gantner, Z., & Schmidt-Thieme, L. (2010). Cost-sensitive learning methods for imbalanced data. International Joint Conference on Neural Networks (IJCNN). Barcelona: IEEE.
Verma, B. P., Verma, V., & Badholia, A. (2022). Hyper-Tuned Ensemble Machine Learning Model for Credit Card Fraud De-tection. International Conference on Inventive Computation Technologies (ICICT). IEEE.
Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., & Jiang, C. (2018, March). Random forest for credit card fraud detection. In 2018 IEEE 15th international conference on networking, sensing and control (ICNSC) (pp. 1-6). IEEE.
Zhang, F., Liu, G., Li, Z., Yan, C., & Jiang, C. (2019). GMM-based Undersampling and Its Application for Credit Card Fraud Detection. International Joint Conference on Neural Networks (IJCNN). Budapest: IEEE.
Zhu, H., Liu, G., Zhou, M., Xie, Y., Abusorrah, A., & Kang, Q. (2020). Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection. Neurocomputing, 407, 50-62.
Zioviris, G., Kolomvatsos, K., & Stamoulis, G. (2022, April 06). Credit card fraud detection using a deep learning multistage model. The Journal of Supercomputing, 78, 14571–14596.
Abdel-Basset, M., Abdel-Fatah, L., & Sangaiah, A. K. (2018). Metaheuristic algorithms: A comprehensive review. Computa-tional intelligence for multimedia big data on the cloud with engineering applications, 185-231.
Ahmad, H., Kasasbeh, B., Aldabaybah, B., & Rawashdeh, E. (2023). Class balancing framework for credit card fraud detec-tion based on clustering and similarity-based selection (SBS). International Journal of Information Technology, 15(1), 325-333.
Ahmad, H., Kasasbeh, B., AL-Dabaybah, B., & Rawashdeh, E. (2023). EFN-SMOTE: An effective oversampling technique for credit card fraud detection by utilizing noise filtering and fuzzy c-means clustering. International Journal of Data and Network Science, 7(3), 1025-1032.
Bin Sulaiman, R., Schetinin, V., & Sant, P. (2022). Review of machine learning approach on credit card fraud detection. Hu-man-Centric Intelligent Systems, 2(1-2), 55-68.
Biswas, M., & Debbarma, S. (2022, June). An Efficient Approach for Credit Card Fraud Identification with the Oversampling Method. In International Conference on Frontiers of Intelligent Computing: Theory and Applications (pp. 273-286). Singa-pore: Springer Nature Singapore.
Cao, W., Wang, X., Ming, Z., & Gao, J. (2018). A review on neural networks with random weights. Neurocomputing, 275, 278-287.
Cheng, R., & Jin, Y. (2015). A Competitive Swarm Optimizer for Large Scale Optimization. IEEE Transactions on Cybernet-ics, 45(2), 191 - 204.
Cherif, A., Badhib, A., Ammar, H., Alshehri , S., Kalkatawi, M., & Imine, A. (2023). Credit card fraud detection in the era of disruptive technologies: A systematic review. Journal of King Saud University - Computer and Information Sciences, 35(1), 145-174.
Esenogho, E., Mienye, I. D., Swart, T. G., Aruleba, K., & Obaido, G. (2022). A Neural Network Ensemble With Feature Engi-neering for Improved Credit Card Fraud Detection. IEEE Access, 10, 16400 - 16407.
Habibi, A., Delavar, M. R., Sadeghian, M. S., Nazari, B., & Pirasteh, S. (2023). A hybrid of ensemble machine learning models with RFE and Boruta wrapper-based algorithms for flash flood susceptibility assessment. International Journal of Applied Earth Observation and Geoinformation, 122, 103401.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004, July). Extreme learning machine: a new learning scheme of feedforward neural networks. In 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541) (Vol. 2, pp. 985-990). Ieee.
Ileberi, E., Sun, Y., & Wang, Z. (2022). A machine learning based credit card fraud detection using the GA algorithm for fea-ture selection. Journal of Big Data, 9(1), 1-17.
Jiao, R., Nguyen, B. H., Xue, B., & Zhang, M. (2023). A Survey on Evolutionary Multiobjective Feature Selection in Classifi-cation: Approaches, Applications, and Challenges. IEEE Transactions on Evolutionary Computation.
Jovanovic, D., Antonijevic, M., Stankovic, M., Zivkovic, M., Tanaskovic, M., & Bacanin, N. (2022). Tuning machine learning models using a group search firefly algorithm for credit card fraud detection. Mathematics, 10(13), 2272.
Karthika, J., & Senthilselvi, A. (2022). Credit Card Fraud Detection based on Ensemble Machine Learning Classifiers. 3rd In-ternational Conference on Electronics and Sustainable Communication Systems (ICESC). Coimbatore: IEEE.
Kaur, B. J., & Kumar, R. (2020). A hybrid approach for credit card fraud detection using naive Bayes and voting classifier. In Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI-2019) (pp. 731-740). Spring-er International Publishing.
Kohavi, R., & John, G. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97.
Lima, R. F., & Pereira, A. (2017). Feature Selection Approaches to Fraud Detection in e-Payment Systems. International Con-ference on Electronic Commerce and Web Technologies (pp. 111-126). Springer.
Liu, H., Zhou, M., & Liu, Q. (2019). An embedded feature selection method for imbalanced data classification. IEEE/CAA Journal of Automatica Sinica, 6(3), 703-715.
Liu, O., Ma, J., Poon, P.-L., & Zhang, J. (2009). On an Ant Colony-Based Approach for Business Fraud Detection. Interna-tional Conference on Intelligent Computing. 5754, pp. 1104–1111. Springer.
Majhi, S. K. (2021). Fuzzy clustering algorithm based on modified whale optimization algorithm for automobile insurance fraud detection. Evolutionary Intelligence, 14, 35-46.
Malik, E. F., Khaw, K. W., Belaton, B., Wong, W. P., & Chew, X. (2022). Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture. Mathematics, 10(9).
Masoud, M., Jaradat, Y., Rababa, E., & Manasrah, A. (2021). Turnover Prediction using Machine Learning: Empirical Study. International Journal of Advances in Soft Computing & Its Applications, 13(1).
Mienye, I. D., & Sun, Y. (2023). A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection. Applied Sciences, 13(12), 7254.
Prasetiyowati, M. I., Maulidevi, N. U., & Surendro, K. (2021). Determining threshold value on information gain feature selec-tion to increase speed and prediction accuracy of random forest. Journal of Big Data, 8(1), 84.
Rakesh, D. K., & Jana, P. (2023). An improved differential evolution algorithm for quantifying fraudulent transactions. Pat-tern Recognition, 141.
Rawashdeh, E., Aljarah , I., & Faris, H. (2021). A cooperative coevolutionary method for optimizing random weight networks and its application for medical classification problems. Journal of Ambient Intelligence and Humanized Computing, 12, 321–342.
Roseline, J. F., Naidu, G. B. S. R., Pandi, V. S., alias Rajasree, S. A., & Mageswari, N. (2022). Autonomous credit card fraud detection using machine learning approach☆. Computers and Electrical Engineering, 102, 108132.
Rtayli, N., & Enneya, N. (2020). Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization. Journal of Information Security and Applications, 55.
Schmidt, W. F., Kraaijveld, M. A., & Duin, R. P. (1992). Feedforward neural networks with random weights. Conference B: Pattern Recognition Methodology and Systems (pp. 1-4). IEEE.
Shenvi, P., Samant, N., Kumar, S., & Kulkarni, V. (2019). Credit Card Fraud Detection using Deep Learning. IEEE 5th Inter-national Conference for Convergence in Technology (I2CT). Bombay: IEEE.
Shirgave, S., Awati, C., More, R., & Patil, S. (2019). A Review On Credit Card Fraud Detection Using Machine Learning. In-ternational Journal Of Scientific & Technology Research, 8(10).
Singh, A., & Jain, A. (2020). Cost-sensitive metaheuristic technique for credit card fraud detection. Journal of Information and Optimization Sciences, 41(6), 1319-1331.
Song, Q., Jiang, H., & Liu, J. (2017). Feature selection based on FDA and F-score for multi-class classification. Expert Systems with Applications, 81, 22-27.
Thai-Nghe, N., Gantner, Z., & Schmidt-Thieme, L. (2010). Cost-sensitive learning methods for imbalanced data. International Joint Conference on Neural Networks (IJCNN). Barcelona: IEEE.
Verma, B. P., Verma, V., & Badholia, A. (2022). Hyper-Tuned Ensemble Machine Learning Model for Credit Card Fraud De-tection. International Conference on Inventive Computation Technologies (ICICT). IEEE.
Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., & Jiang, C. (2018, March). Random forest for credit card fraud detection. In 2018 IEEE 15th international conference on networking, sensing and control (ICNSC) (pp. 1-6). IEEE.
Zhang, F., Liu, G., Li, Z., Yan, C., & Jiang, C. (2019). GMM-based Undersampling and Its Application for Credit Card Fraud Detection. International Joint Conference on Neural Networks (IJCNN). Budapest: IEEE.
Zhu, H., Liu, G., Zhou, M., Xie, Y., Abusorrah, A., & Kang, Q. (2020). Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection. Neurocomputing, 407, 50-62.
Zioviris, G., Kolomvatsos, K., & Stamoulis, G. (2022, April 06). Credit card fraud detection using a deep learning multistage model. The Journal of Supercomputing, 78, 14571–14596.