Examples of such methods include Multi-Label Random Under-Sampling (ML-RUS) and Multi-Label Random Over-Sampling (ML-ROS), which concentrate on the frequency of individual labels rather than on entire label sets. These methods specifically target instances that contain one or more minority labels. In this study, we utilize ML-RUS and ML-ROS . To determine the imbalance level of each label, we use metrics such as IRLbl, MeanIR, and CVIR. Labels with IRLbl values higher than MeanIR are referred to as minority labels, indicating which instances should be cloned or not removed. Labels with IRLbl values lower than MeanIR are identified as majority labels.