Class imbalance is a problem of crucial challenge in many real-world machine learning
applications. Traditional machine learning algorithms are likely to produce good accuracy …

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 …

Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

Imbalanced data presents many difficulties, as the majority of learners will be prejudice
against the majority class, and in severe cases, may fully disregard the minority class. Over …

Numerous imbalanced datasets exist in modern machine learning dilemmas. Challenges of
generalization and fairness stem from the existence of underrepresented classes with …

The impact of Internet of Things (IoT) has become increasingly significant in smart
manufacturing, while deep generative model (DGM) is viewed as a promising learning …

Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from
achieving satisfactory results. ID is the occurrence of a situation where the quantity of the …

Imbalanced learning is a fundamental challenge in data mining, where there is a
disproportionate ratio of training samples in each class. Over-sampling is an effective …

Imbalanced data with a skewed class distribution are common in many real-world
applications. Deep Belief Network (DBN) is a machine learning technique that is effective in …

The data imbalance problem is a frequent bottleneck in the classification performance of
neural networks. In this paper, we propose a novel supervised discriminative feature …