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- W4285359208 abstract "With the speedy expansion of e-commerce, credit cards have also become rising vogue, and that makes online transactions sleek and suitable. In conjunction with rising in online transactions, credit card fraud also increasing, which contributes to losses incurred yearly. As a result, many deep and machine learning methods are produced to fix such as problems like Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and other algorithms, but the current models are still not accurate. Moreover, sometimes the used datasets still need further preprocessing, since that has been approved the important role of feature engineering in performance optimization. In this paper, effective feature engineering and feature selection methods have been produced for preprocessing the raw dataset, which was transformed with Exploratory Data Analysis (EDA). Then LightGBM, XGboost, and Random forest classifiers are used for fraud detection. Experiments show that the LightGBM and XGboost models achieved the best accuracy with 100% after applying further preprocessing on the dataset. Index Terms— Fraud detection, Feature engineering, Machine Learning, LightGBM, Random forest, XGboost." @default.
- W4285359208 created "2022-07-14" @default.
- W4285359208 date "2021-09-30" @default.
- W4285359208 modified "2023-10-16" @default.
- W4285359208 title "An Efficient Feature Engineering Method for Fraud Detection in E-commerce" @default.
- W4285359208 doi "https://doi.org/10.33103/uot.ijccce.21.3.4" @default.
- W4285359208 hasPublicationYear "2021" @default.
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