Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …

Class imbalance with overlap is a very challenging problem in electronic fraud transaction
detection. Fraudsters have racked their brains to make a fraud transaction as similar as a …

Most existing classification approaches assume the underlying training set is evenly
distributed. In class imbalanced classification, the training set for one class (majority) far …

The class imbalance problems have been reported to severely hinder classification
performance of many standard learning algorithms, and have attracted a great deal of …

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 …

Class imbalance is one of the challenges of machine learning and data mining fields.
Imbalance data sets degrades the performance of data mining and machine learning …

Canonical machine learning algorithms assume that the number of objects in the considered
classes are roughly similar. However, in many real-life situations the distribution of examples …

Computational synthesize of the metabolic pathway is take low cost while comparing with
the direct trial and error laboratory process. In real world data, more or less all datasets …

Nowadays, handling of the imbalance data is a major challenge. Imbalanced data set
means the instances of one class are much more than the instances of another class where …