Imbalanced Data Classification: A Critical Challenge in Machine Learning

Currently, classifiers used for data classification are very mature. However, when the number of samples in each category differs significantly, the classification performance of the classifier will noticeably decline. In practical applications, imbalanced data classification is very common, such as fault detection, cancer screening, and network intrusion detection. Therefore, it is very important to address the problem of imbalanced data classification.