A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection

Abstract

With the rapid developments in electronic commerce and digital payment technologies, credit card transactions have increased significantly. Machine learning (ML) has been vital in analyzing customer data to detect and prevent fraud. However, the presence of redundant and irrelevant features in most real-world credit card data degrades the performance of ML classifiers. This study proposes a hybrid feature-selection technique consisting of filter and wrapper feature-selection steps to ensure that only the most relevant features are used for machine learning. The proposed method uses the information gain (IG) technique to rank the features, and the top-ranked features are fed to a genetic algorithm (GA) wrapper, which uses the extreme learning machine (ELM) as the learning algorithm. Meanwhile, the proposed GA wrapper is optimized for imbalanced classification using the geometric mean (G-mean) as the fitness function instead of the conventional accuracy metric. The proposed approach achieved a sensitivity and specificity of 0.997 and 0.994, respectively, outperforming other baseline techniques and methods in the recent literature.