Abstract In Machine Learning, a data set is imbalanced when the class proportions are
highly skewed. Imbalanced data sets arise routinely in many application domains and pose …

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 …

Building useful classification models can be a challenging endeavor, especially when
training data is imbalanced. Class imbalance presents a problem when traditional …

Class imbalance ubiquitously exists in real life, which has attracted much interest from
various domains. Direct learning from imbalanced dataset may pose unsatisfying results …

Ensemble technique and under-sampling technique are both effective tools used for
imbalanced dataset classification problems. In this paper, a novel ensemble method …

In imbalanced learning methods, resampling methods modify an imbalanced dataset to form
a balanced dataset. Balanced data sets perform better than imbalanced datasets for many …

The scenario of classification with imbalanced datasets has gained a notorious significance
in the last years. This is due to the fact that a large number of problems where classes are …

In the field of artificial intelligence, classification algorithms tend to be biased toward the
majority class samples when encountering imbalanced data, resulting in low recognition …

Imbalanced learning is one of the substantial challenging problems in the field of data
mining. The datasets that have skewed class distribution pose hindrance to conventional …

Undersampling is a popular method to solve imbalanced classification problems. However,
sometimes it may remove too many majority samples which may lead to loss of informative …