The imbalanced dataset is a challenging issue in many classification tasks. Because it leads
a machine learning algorithm to poor generalization and performance. The imbalanced …

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 …

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 …

Training a machine learning algorithm from a class-imbalanced dataset is an inherently
challenging task. The task becomes more challenging when compounded by high …

Credit scoring is an imbalanced binary classification problem, where the number of
instances of bad customers is much less than that of good customers. Traditional …

Data imbalance in Machine Learning refers to an unequal distribution of classes within a
dataset. This issue is encountered mostly in classification tasks in which the distribution of …

Imbalanced data pose a tremendous challenge to standard machine learning classifiers
which assume balanced training data. These methods are inclined to accurately classify the …

Machine learning classifiers often stumble over imbalanced datasets where classes are not
equally represented. This inherent bias towards the majority class may result in low …

Data quality is an essential element necessary for the development of a successful machine-
learning project. One of the biggest challenges in various real-world application domains is …

Class imbalanced datasets are common in real-world applications that range from credit
card fraud detection to rare disease diagnostics. Several popular classification algorithms …