AN EXPLORATION OF FEATURE SELECTION TECHNIQUES FOR CREDIT CARD FRAUD DETECTION

AN EXPLORATION OF FEATURE SELECTION TECHNIQUES FOR CREDIT CARD FRAUD DETECTION

Abstract:
Credit card fraud has become a significant concern for financial institutions and cardholders due to its increasing prevalence and detrimental impact. To combat this issue, machine learning techniques have been widely adopted for credit card fraud detection. However, the success of these techniques heavily relies on the selection of appropriate features that capture the underlying patterns of fraudulent transactions.

This paper presents an exploration of feature selection techniques for credit card fraud detection. The objective is to identify the most relevant features that contribute to the accurate classification of fraudulent transactions while minimizing the computational complexity and potential overfitting.

The study compares and evaluates various feature selection methods, including filter-based, wrapper-based, and embedded techniques. Filter-based methods, such as information gain and chi-square, assess the statistical significance of each feature independently of the classification model. Wrapper-based methods, such as recursive feature elimination and genetic algorithms, incorporate the classification model’s performance during the feature selection process. Embedded methods, such as L1 regularization and decision tree-based feature importance, integrate feature selection within the model training process itself.

To conduct the exploration, a comprehensive dataset comprising both fraudulent and legitimate credit card transactions is utilized. The dataset is preprocessed to handle missing values, outliers, and class imbalance. Feature selection techniques are then applied to identify the most discriminative features.

The performance of each feature selection technique is evaluated using various classification models, such as logistic regression, support vector machines, and random forests. Evaluation metrics, including accuracy, precision, recall, and F1-score, are used to assess the effectiveness of feature selection in detecting credit card fraud accurately.

The results of the study provide insights into the effectiveness of different feature selection techniques for credit card fraud detection. The findings contribute to the development of robust and efficient fraud detection systems that can effectively mitigate the financial losses associated with fraudulent transactions.

Keywords: credit card fraud detection, feature selection, machine learning, filter-based methods, wrapper-based methods, embedded methods, classification models.

AN EXPLORATION OF FEATURE SELECTION TECHNIQUES FOR CREDIT CARD FRAUD DETECTION

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