Since minority samples are substantially less common than majority samples, many
industrial applications, such as credit card fraud detection (CCFD) and defective part …

The class imbalance problem exists in many real-world applications such as fraud detection,
medical diagnosis and spam filtering, and seriously influences the performance of learning …

For classification problem, the training data will significantly influence the classification
accuracy. However, the data in real-world applications often are imbalanced class …

Imbalanced data classification problem has always been a popular topic in the field of
machine learning research. In order to balance the samples between majority and minority …

Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine
learning based fraudulent transaction detection systems are very effective in real-world …

The class imbalance problem occurs when one class far outnumbers the other classes,
causing most traditional classifiers perform poorly on the minority classes. To tackle this …

Classification problems with imbalanced data are challenging because traditional classifiers
tend to misclassify minority samples. This paper introduces a novel two-phase method in the …

For imbalanced datasets, the focus of classification is to identify samples of the minority
class. The performance of current data mining algorithms is not good enough for processing …

A clustering-based undersampling (CUS) and distance-based near-miss method are widely
used in current imbalanced learning algorithms, but this method has certain drawbacks. In …

The class imbalance problem usually occurs in real applications. The class imbalance is that
the amount of one class may be much less than that of another in training set. Under …