# Application of Label Correlation in Multi-Label Classification: A Survey
## 5 Applications of Label Correlation to MLC Subproblems  
As research in the field of MLL progresses, many MLC problems under various settings have attracted the attention of researchers. Collecting data with comprehensive annotation information is often challenging, significantly increasing the complexity and workload of manual annotation. Given the scarcity of annotated data and the complexity of the annotation process, researchers have started to focus on solving MLC problems under limited supervision conditions. Examples include MLC with missing labels, semi-supervised MLC, and Partial Multi-Label Learning (PML). The missing labels problem posits that only a portion of the true labels is available, leaving some true labels unannotated. In contrast, partial multi-label problems assume that each instance is linked to a candidate label set that may contain incorrect labels. These scenarios aim to develop a multi-label classifier capable of learning from such ambiguous data and assigning relevant labels to test instances. Semi-supervised MLC integrates fully labeled and unlabeled data to train a multi-label classifier. As the volume of data rapidly increases, multi-label data obtained from various real-world applications often features a vast feature space that contains numerous irrelevant and redundant features. This high-dimensional characteristic can negatively impact classification tasks. In MLL, the labels for each category may be determined by specific subsets of features. Consequently, some researchers have begun to design multi-label feature selection algorithms aimed at identifying features that are rich in information and representative, thereby achieving dimensionality reduction and enhancing the performance of classifiers. In these MLC subproblems, label correlations also play an important role. Label correlations reflect the intrinsic connections between different labels and can be utilized to optimize various aspects of MLC tasks. For example, existing studies have successfully utilized label correlations to reconstruct missing labels, aiding in solving the MLML problem [66; 80; 81]. Additionally, [PERSON] et al. [22] proposed a method called Hierarchical Label Set Expansion (HLSE) to address some missing label levels within the MeSH label hierarchy. This method utilizes the hierarchical relationships between labels by adding missing ancestor labels to the label set of each sample, thereby expanding the label set for each document. The aim is to maintain consistency in the hierarchical structure of the training dataset, thereby enhancing the standardization and effectiveness of the training process. [PERSON] et al. [72] introduced a model named Imbalanced and Missing Multi-label Data Learning with Global and Local Structure (IMLGL), which is designed to address both missing labels and class imbalance issues in MLL simultaneously. Multi-label feature selection is also crucial in addressing the high-dimensional challenges associated with MLL. Research indicates that label correlation can aid the label-specific feature selection process [54; 70; 76; 82], thereby significantly enhancing the accuracy and efficiency of feature selection. Previous studies often treated the label space as static; however, in practical applications, labels are frequently dynamic. To address this, [PERSON] et al. [83] proposed the LLSL method, which develops a feature transformation mechanism to integrate specific features for each dynamic label. In the field of Semi-Supervised MLC (SS-MLC), a few studies have developed graph-based learning methods by constructing label relationship graphs [84]. Additionally, in MLIC tasks, [PERSON] et al. [85] proposed a semi-supervised remote sensing image classification method that combines deep learning with predictive clustering trees, effectively leveraging unlabeled data to enhance classification performance. The PML problem aims to address the challenge of training data that contains both true and noisy labels.