# Application of Label Correlation in Multi-Label Classification: A Survey
## 3 Label Correlation Mining Methods
### Label Graph-Based Methods
[2020] proposed using heterogeneous graph Transformers to construct and learn label heterogeneous graphs, which combine label hierarchical structures with statistical relationships of label co-occurrence. [PERSON] et al. [2020] proposed a Transformer-based model called TLC  
Figure 3: The HEX structure diagram illustrates three distinct types of relationships among different labels [44]. XML, designed to address the XMTC task by leveraging label correlations within the label space. This model consists of three modules: First, it constructs a label association graph using the semantic and co-occurrence information of labels, grouping highly related labels together; Second, it applies a cluster association learning algorithm that utilizes Graph Convolutional Networks (GCN) to extract associations between different clusters; Finally, it employs a label interaction learning algorithm to integrate the original label predictions with information from neighboring labels. In contrast to the aforementioned methods, [PERSON] et al. [54] proposed the MLSF method, which models positive label correlations through meta-label learning. This method views meta-labels as collections of multiple labels, where member labels exhibit strong dependencies among themselves, while the dependencies with non-member labels are weaker. To identify the hidden meta-labels in the label space, the approach combines information from both the label space and the instance space when constructing the association matrix for the label graph, treating the association matrix as the edge matrix of the label graph. Subsequently, the label grouping problem is transformed into a graph cut problem, utilizing spectral clustering techniques to partition the label graph into K subgraphs, grouping highly correlated labels into a single subgraph, or meta-label. To leverage the correlations preserved within the meta-labels, classifier chains are constructed for each meta-label. However, the limitation of the MLSF method lies in the fact that when certain labels are missing from the training data, the meta-label learning phase may fail to capture comprehensive and accurate label relationships. Overall, early research often leveraged external knowledge sources to construct hierarchical relationships among labels to exploit label correlations. However, the drawback of this approach is that when dealing with domain-specific multi-label datasets, existing external knowledge sources find it difficult to provide prior knowledge of label correlations for all labels within the dataset. Additionally, due to the varying semantic connotations of labels across different domains, the defined label relationships also exhibit significant differences. Therefore, directly adopting generic external knowledge sources in domain-specific datasets may lead to the introduction of incorrect label relationships. Although methods utilizing dataset statistics, such as label co-occurrence information, have been widely applied due to their generality and simplicity, these methods often yield biased label correlations in real-world scenarios with imbalanced label data, failing to reflect the true label correlations within the dataset. To overcome this limitation, supplementing statistical information with external prior knowledge has emerged as a viable approach. This method helps enhance the accuracy and reliability of label relationships.