# A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection
## 1 Introduction  
Over the years, electronic payments (e-payments) have been the most common payment option due to technological advancements and the development of several electronic funding methods [1]. E-payment systems are essential to the present competitive financial sector and are mostly performed using credit cards [2]. The introduction of credit cards has resulted in convenient and seamless e-payments. A recent study stated that in the second quarter of 2021, Mastercard and Visa issued 1131 million and 1156 million cards, respectively [3]. However, the rise of credit card usage globally has increased the fraud rate, affecting consumers and merchants [4]. For instance, a report stated that financial losses due to credit and debit cards are among the leading causes of losses in the financial sector [3]. Therefore, developing efficient credit card fraud-detection systems is necessary to reduce such losses. Machine learning algorithms have been widely employed to detect credit card fraud [5; 6; 7]. Meanwhile, there have been enormous datasets with very high dimensions due to the advent of big data and the Internet of Things (IoT) [8; 9]. Furthermore, some features in these datasets might be redundant or less significant to the response variable. Using such features for machine learning could increase the complexity of the model and lead to overfitting [10]. Therefore, to handle the high dimensionality issue, an approach containing dimensionality reduction, such as feature selection, is necessary to obtain valuable insights and make accurate predictions [11]. Feature-selection techniques aim to identify the most important attributes needed to develop a well-performing machine learning model [12; 13], ensuring improved classification performance and reduced computational complexity by removing irrelevant andredundant features. Feature selection techniques are usually grouped into three methodological groups: filters, wrappers, and embedded methods [10; 14]. The internal workings and configuration of the various feature-selection methods make them suitable for different applications. Filter methods employ attribute ranking to determine the most informative features. Features that attain scores above a given threshold are selected, and those below the threshold are discarded. After identifying the most important features, they can be fed as input to the learning algorithm. Filter methods vary from wrapper and embedded methods as they are not dependent on a classifier and are, therefore, independent of the classifier's bias [15]. However, wrapper methods use an ML classifier's performance as the evaluation metric in selecting the most relevant feature set. Wrapper methods usually lead to better classification performance than filter techniques because the feature-selection procedure is optimized for the chosen classification algorithm [16; 17]. Generally, wrapper methods employ a search strategy to identify the candidate subsets. The classifier's performance on the various feature subsets is measured, and the subset that leads to the highest performance is selected as the most informative subset. Examples of wrapper-based feature selection techniques include the Boruta algorithm, forward selection, backward elimination, and the genetic algorithm. Embedded methods select the features that enhance the model's performance during training. The feature selection is incorporated into the learning procedure [13]. Unlike wrapper methods, this type of feature selection aims to reduce the time used in training different subsets. Embedded methods include random forest, decision tree, gradient boosting, elastic net, and LASSO [10]. Meanwhile, the GA wrapper is an effective method for feature selection, with applications in diverse domains, including natural language processing (NLP) [18], fraud detection [19], sentiment analysis [20], and medical diagnosis [21]. This study proposes a hybrid feature-selection approach, combining the IG-based filter and GA-based wrapper techniques.