Sharif Digital Repository

One of the major challenges in artificial intelligence is the presence of imbalanced data. Imbalanced data occurs when the number of samples in some classes is significantly lower than in others. This imbalance can lead to bias in machine learning models, as models tend to learn better and more accurately from classes with more samples. As a result, they may perform poorly when classifying samples from minority classes. This issue becomes particularly important when minority classes play a critical role in sensitive applications such as healthcare or security. In these cases, it is essential to pay close and fair attention to the minority classes to avoid unjust outcomes. In recent years,...