Overall, the selection of features for credit card fraud detection requires careful consideration and analysis of the data to ensure that the machine learning model can effectively identify fraudulent transactions while minimizing false positives and false negatives.
May 13, 2024
The Effect of Feature Selection on Credit Card Fraud Detection Success
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Abstract: In this study, a deep learning based credit card fraud detection system has been designed and implemented. The information provided by credit card ...
A deep learning based credit card fraud detection system was designed and implemented and new features were created by grouping the previous transactions ...
Using feature selection algorithm based on rough sets theory, six main indicators were identified as the most effective factors. The fuzzy expert system was ...
In order to increase the success of the model, new features were created by grouping the previous transactions according to features such as merchant category, ...
This study focuses on proposing a framework for the detection of credit card frauds by applying machine learning techniques like Random Forest (RF) and Naïve ...
Jan 17, 2023 · Feature extraction is used to derive a richer set of reduced dataset features, while data sampling is used to mitigate class imbalance. In this ...
The paper investigates the effect of feature ranking of two imbalanced credit card fraud data on four machine learning techniques using filter approach. Credit ...
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May 13, 2024 · Abstract: A comprehensive overview and implementation of a credit card fraud detection system using machine learning for feature selection ...
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Jul 31, 2024 · This research achieves 98.8% accuracy in feature subset generation and 98.5% accuracy in credit card fraud detection.