In the context of high-dimensional credit card fraud data, researchers and practitioners
commonly utilize feature selection techniques to enhance the performance of fraud detection …

Training a machine learning algorithm on a class-imbalanced dataset can be a difficult task,
a process that could prove even more challenging under conditions of high dimensionality …

Many problems in artificial intelligence require adaptively making a sequence of decisions
with uncertain outcomes under partial observability. Solving such stochastic optimization …

Output thresholding is well-suited for addressing class imbalance, since the technique does
not increase dataset size, run the risk of discarding important instances, or modify an …

The yearly increase in incidents of credit card fraud can be attributed to the rapid growth of e-
commerce. To address this issue, effective fraud detection methods are essential. Our …

Research into machine learning methods for fraud detection is of paramount importance,
largely due to the substantial financial implications associated with fraudulent activities. Our …

Robot planning is the process of selecting a sequence of actions that optimize for a task=
specific objective. For instance, the objective for a navigation task would be to find collision …

Abstract Machine learning algorithms efficiently trained on intrusion detection datasets can
detect network traffic capable of jeopardizing an information system. In this study, we use the …

CSE-CIC-IDS2018 is an intrusion detection dataset containing roughly 16,000,000 normal
and anomalous instances, with about 17% of these instances representing attack traffic. Our …

Identifying fraudulent activities in credit card transactions is an inherent component of
financial computing. The focus of our research is on the Credit Card Fraud Detection …