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- Title
Optimizing Credit Card Fraud Detection: Classifier Performance and Feature Selection Empowered by Grasshopper Algorithm.
- Authors
Gupta, Manu Jyoti; Sehgal, Parveen
- 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.
- Subjects
CREDIT card fraud; FEATURE selection; FRAUD investigation; GRASSHOPPERS; INTRUSION detection systems (Computer security); ALGORITHMS
- Publication
International Journal of Performability Engineering, 2024, Vol 20, Issue 3, p177
- ISSN
0973-1318
- Publication type
Article
- DOI
10.23940/ijpe.24.03.p6.177185