Resampling methods are often used to address imbalanced data in multi-label datasets (MLDs). These methods involve preprocessing the MLDs by either removing samples from the majority label (undersampling) or creating new samples for the minority label (oversampling) . Sometimes, a combination of both methods is used. These methods can be categorized as random or heuristic, depending on how samples are added or removed. Random methods choose samples to delete or create randomly, while heuristic methods use specific rules to identify and create the right instances. In this study, we specifically utilize random resampling methods. Random resampling methods for multi-label classification (MLC) can be based on techniques such as the Label Powerset (LP) transformation, Binary Relevance (BR) methods, and various imbalance measures. However, LP-based resampling may be limited due to the sparsity of labels in multi-label datasets (MLDs). In these datasets, there can be as many unique label combinations as there are instances, leading to scenarios where all label sets can simultaneously be classified as both majority and minority cases. An alternative approach to address this challenge is focusing on each individual label's imbalance level.