Imbalanced Data Classification: Challenges and Solutions for Enhanced Performance

Currently, classifiers for data classification are very mature. However, when the number of samples between different classes varies significantly, the classification performance of the classifier will noticeably decrease. 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 classification problem of imbalanced data.