Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences. The monitoring and classification of these equipment anomalies can be performed using fault detection and classification (FDC). However, class imbalance in semiconductor process data poses a significant obstacle to the introduction of FDC into semiconductor equipment. Overfitting can occur in machine learning due to the diversity and imbalance of datasets for normal and abnormal.