For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …

Abstract The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known
resampling strategy that has been successfully used for dealing with the class-imbalance …

Learning and mining from imbalanced datasets gained increased interest in recent years.
One simple but efficient way to increase the performance of standard machine learning …

The algorithm of C4. 5 decision tree has the advantages of high classification accuracy, fast
calculation speed and comprehensible classification rules, so it is widely used for medical …

Synthetic minority oversampling technique (SMOTE) has been widely used in dealing with
the imbalance classification problem in the machine learning field. However, classical …

Background Clinical diagnosis based on machine learning usually uses case samples as
training samples, and uses machine learning to construct disease prediction models …

Data imbalance is a common phenomenon in machine learning. In the imbalanced data
classification, minority samples are far less than majority samples, which makes it difficult for …

Corporate financial distress forecasts are important for companies, investors and regulatory
authorities. However, as most financial distress forecast (FDF) models in previous studies …

Class imbalance occurs in classification problems in which the “normal” cases, or instances,
significantly outnumber the “abnormal” instances. Training a standard classifier on …

In many practical applications, the data are class imbalanced. Accordingly, it is very
meaningful and valuable to investigate the classification of imbalanced data. In the …