Optimizing Credit Card Fraud Detection: Classifier Performance and Feature Selection Empowered by Grasshopper Algorithm

doi:10.23940/ijpe.24.03.p6.177185

Abstract

Abstract: Identifying fraud with credit cards is still a significant obstacle in economic safety, requiring precise and effective classification models to reduce the dangers connected with fraudulent transactions. The evaluation of several classifiers, such as "MLP," "SVM," "Random Forest," and "Logistic Regression," is examined in this paper using extensive evaluation criteria like Precision, Recall, F-measure, and Accuracy. The dataset encompasses average values for these metrics, providing insights into the classifiers' abilities to predict positive and negative instances accurately. Understanding the Grasshopper algorithm's function in enhancing feature selection for credit card fraud detection is essential to this research. The results highlight 'MLP' as a standout performer across multiple metrics, showcasing its precision (0.942), recall (0.891), F-measure (0.915), and accuracy (95.49%). 'Random Forest' and 'Logistic Regression' demonstrate commendable results, reflecting their suitability for this task. However, 'SVM' slightly lags in comparison. The results highlight the complementary roles that good feature selection and suitable classifier selection play in improving the identification of credit card fraud systems. The robustness of 'MLP' and high accuracy position it as a promising option for addressing the complexities of credit card fraud. This study highlights the importance of careful feature selection and classifier optimization in building effective fraud detection systems that can successfully address changing fraudulent actions.

Key words: credit card fraud detection, machine learning, classification, grasshopper

Manu Jyoti Gupta and Parveen Sehgal. Optimizing Credit Card Fraud Detection: Classifier Performance and Feature Selection Empowered by Grasshopper Algorithm [J]. Int J Performability Eng, 2024, 20(3): 177-185.

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References

[1] Cherif A., Badhib A., Ammar H., Alshehri S., Kalkatawi M., andImine A.Credit Card Fraud Detection in the Era of Disruptive Technologies: A Systematic Review. Journal of King Saud University-Computer and Information Sciences, vol. 35, no. 1, pp. 145-174, 2023.
[2] Alarfaj F.K., Malik I., Khan H.U., Almusallam N., Ramzan M., andAhmed M.Credit Card Fraud Detection using State-of-The-Art Machine Learning and Deep Learning Algorithms. IEEE Access, vol. 10, pp. 39700-39715, 2022.
[3] Alfaiz, N.S. and Fati, S.M.Enhanced Credit Card Fraud Detection Model using Machine Learning, Electronics, vol. 11, no. 4, pp. 662, 2022.
[4] Malik E.F., Khaw K.W., Belaton B., Wong W.P., andChew X.Credit Card Fraud Detection using a New Hybrid Machine Learning Architecture. Mathematics, vol. 10, no. 9, pp.1480, 2022.
[5] Roseline, J.F., Naidu, G.B.S.R., Pandi, V.S., alias Rajasree, S.A., and Mageswari, N. Autonomous Credit Card Fraud Detection using Machine Learning Approach☆. Computers and Electrical Engineering, vol. 102, pp. 108132, 2022.
[6] Jovanovic D., Antonijevic M., Stankovic M., Zivkovic M., Tanaskovic M., andBacanin N.Tuning Machine Learning Models using a Group Search Firefly Algorithm for Credit Card Fraud Detection. Mathematics, vol. 10, no. 13, pp. 2272, 2022.
[7] Han S., Zhu K., Zhou M., andCai X.Competition-Driven Multimodal Multiobjective Optimization and Its Application to Feature Selection for Credit Card Fraud Detection. IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 12, pp. 7845-7857, 2022.
[8] Geetha, N. and Dheepa, G.Transaction Fraud Detection using Artificial Bee Colony (ABC) Based Feature Selection and Enhanced Neural Network (ENN) Classifier. International Journal of Mechanical Engineering, vol. 7, no. 3, 2022.
[9] Ganji V.R., Chaparala A., andSajja R.Shuffled Shepherd Political Optimization‐Based Deep Learning Method for Credit Card Fraud Detection. Concurrency and Computation: Practice and Experience, vol. 35, no. 10, pp. e7666, 2023.
[10] Tayebi, M. and El Kafhali, S. Performance Analysis of Metaheuristics Based Hyperparameters Optimization for Fraud Transactions Detection.Evolutionary Intelligence, pp. 1-19, 2022.
[11] Arun, G.K. and Rajesh, P.Design of Metaheuristic Feature Selection with Deep Learning Based Credit Card Fraud Detection Model. In2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), IEEE, pp. 191-197, 2022.
[12] Almazini H.Enhanced Grey Wolf Optimization Algorithm for Feature Selection in Anomaly Detection,Doctoral thesis, Universiti Utara Malaysia, 2022.
[13] Arun, G.K. and Rajesh, P.Hunger Search Algorithm with Optimal Deep Learning Driven Credit Card Fraud Detection and Classification Model. Mathematical Statistician and Engineering Applications, vol. 71, no. 4, pp. 387-406, 2022.
[14] Esenogho E., Mienye I.D., Swart T.G., Aruleba K., andObaido G.A Neural Network Ensemble with Feature Engineering for Improved Credit Card Fraud Detection. IEEE Access, vol. 10, pp. 16400-16407, 2022.
[15] Stankovic M., Bacanin N., Zivkovic M., Jovanovic D., Antonijevic M., Bukmira M., andStrumberger I.Feature Selection and Extreme Learning Machine Tuning by Hybrid Sand Cat Optimization Algorithm for Diabetes Classification. In International Conference on Modelling and Development of Intelligent Systems, Cham: Springer Nature Switzerland, pp. 188-203, 2022.
[16] Wang M., Li X., andChen L.An Enhance Multimodal Multiobjective Optimization Genetic Algorithm with Special Crowding Distance for Pulmonary Hypertension Feature Selection. Computers in Biology and Medicine, vol. 146, pp. 105536, 2022.
[17] Saheed Y.K.A Binary Firefly Algorithm Based Feature Selection Method on High Dimensional Intrusion Detection Data. In Illumination of Artificial Intelligence in Cybersecurity and Forensics, Cham: Springer International Publishing, pp. 273-288, 2022.
[18] Ileberi E., Sun Y., andWang Z.A Machine Learning Based Credit Card Fraud Detection using the GA Algorithm for Feature Selection. Journal of Big Data, vol. 9, no. 1, pp. 1-17, 2022.
[19] Sehgal P., Gupta S., andKumar D.Application of Neural Networks in Predictive Data Mining for Insurance, International Journal of Latest Trends in Engineering and Technology, vol. 1, no. 1, 2012.