Abstract
With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.
Highlights
Classification has been a key application area of machine learning
Not easy to use single or few input variables only to differentiate multiple classes to their fullest [1]. In many classifiers such as neural networks, k-nearest neighbors, Support Vector Machine (SVM), and Naïve Bayes (NB), the underlying assumption is that training data samples contain a valid representation of the population of interest, which normally require a balanced sample distribution [3]
SVM, NN, and decision trees were used in the multi-classifier system (MCS) model, which was applied to resource-based enterprises in China
Summary
Classification has been a key application area of machine learning. A classifier learns a mathematical model from training data samples that maps input features to the target classes or labels [1]. Credit card fraud detection using a BKS model classifiers has become one of the most significant methodologies to improve the classification performance. All classifiers provide their predictions of the class of an incoming data sample, and these predictions are analyzed and combined using some fusion strategy [4]. In this regard, selections of appropriate classifiers for constructing an ensemble classification model remain a difficult task [2].
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