X-SHAoLIM: Novel Feature Selection Framework for Credit Card Fraud Detection | ||
| Journal of AI and Data Mining | ||
| دوره 12، شماره 1، فروردین 2024، صفحه 57-66 اصل مقاله (1.55 M) | ||
| نوع مقاله: Applied Article | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2024.13630.2480 | ||
| نویسندگان | ||
| Sajjad Alizadeh Fard؛ Hossein Rahmani* | ||
| School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran. | ||
| چکیده | ||
| Fraud in financial data is a significant concern for both businesses and individuals. Credit card transactions involve numerous features, some of which may lack relevance for classifiers and could lead to overfitting. A pivotal step in the fraud detection process is feature selection, which profoundly impacts model accuracy and execution time. In this paper, we introduce an ensemble-based, explainable feature selection framework founded on SHAP and LIME algorithms, called "X-SHAoLIM". We applied our framework to diverse combinations of the best models from previous studies, conducting both quantitative and qualitative comparisons with other feature selection methods. The quantitative evaluation of the "X-SHAoLIM" framework across various model combinations revealed consistent accuracy improvements on average, including increases in Precision (+5.6), Recall (+1.5), F1-Score (+3.5), and AUC-PR (+6.75). Beyond enhanced accuracy, our proposed framework, leveraging explainable algorithms like SHAP and LIME, provides a deeper understanding of features' importance in model predictions, delivering effective explanations to system users. | ||
| کلیدواژهها | ||
| Machine learning؛ feature selection؛ ensemble learning؛ explainable AI؛ data mining | ||
| مراجع | ||
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