Imbalanced Data Classification: Challenges and Solutions

Currently, classifiers for data classification have become very mature. However, when the number of samples between different categories differs significantly, the classification performance of the classifier will decrease significantly. 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 solve the problem of imbalanced data classification.