# Application of Label Correlation in Multi-Label Classification: A Survey
## 3 Label Correlation Mining Methods
### Label Graph-Based Methods
When label hierarchy structures exist naturally or can be obtained externally, integrating this knowledge to guide traditional supervised learning is beneficial. In practical applications, such as text classification [46] and gene function prediction [47], introducing label hierarchies can help to improve prediction accuracy. These methods typically assume a hierarchical structure for labels and use hierarchical clustering [48] or Bayesian networks [49] to construct label hierarchy graphs. [PERSON] et al. [22] proposed the Hierarchical Deep Neural Network (HDNN), which is specifically well-suited for XMTC tasks and for large-scale datasets with hierarchical label structures, such as the PubMed dataset. The researchers argue thatwhen the label hierarchy is known, embedding it directly into the network topology not only improves classification performance but also enhances the interpretability of results, especially in terms of revealing hierarchical relationships. However, in many application scenarios, it is difficult to form a standardized hierarchical structure between labels, or a hierarchical structure may not even exist. Additionally, when it comes to domain knowledge, external knowledge sources may not provide comprehensive label relationships, and manually constructing a label hierarchy graph requires expert knowledge and is time-consuming. Given the limited generality of label hierarchy graphs, recent works have shifted towards using ordinary graph structures to model more commonly occurring label co-occurrence relationships. In many fields, MLC tasks often lack a predefined label graph structure, which necessitates constructing a label correlation matrix in a data-driven way to build a label graph. Using this graph, some approaches use Graph Convolutional Networks (GCN) to encode label correlations. [PERSON] et al. [2020] proposed constructing a label graph based on label co-occurrence data and utilized DeepWalk to learn low-dimensional representations of label nodes, effectively addressing label sparsity in large-scale MLC. [PERSON] et al. [2021] introduced the ML-GCN framework, which captures global label correlations by leveraging insights from co-occurrence data. This approach uses GCN to map label word embeddings to a set of interdependent target classifiers, which are directly applied to image features for classification. Similarly, [PERSON] et al. [2021] utilized GCN's mapping function to learn interdependent object classifiers, aiming to model higher-order label relationships and enhance performance in text-based MLC models. To improve the accuracy of chest X-ray image classification, [PERSON] et al. [2020] employed a data-driven approach to create two types of association matrices: a local adaptive graph and a global co-occurrence graph. These matrices are used to capture local correlations between categories and global co-occurrence relationships between categories, respectively. However, estimating label correlations by co-occurring labels in training data may lead to overfitting. Furthermore, when certain labels have few positive instances in the training data, the co-occurrence information of labels is not reliable. To address this, [PERSON] et al. [2021] proposed the KSSNet method, which overlays a prior knowledge-based label graph onto a statistical-based label graph to construct the final label graph, instead of solely relying on label co-occurrence information as in ML-GCN. Additionally, unlike traditional graph-enhanced CNN approaches that utilize label system information only in the final recognition stage, the KSSNet method introduces shallow, medium, and deep lateral connections between CNN and GCN, injecting label system information into the backbone CNN for label perception in feature learning. In contrast to schemes introducing prior knowledge, [PERSON] et al. [2020] addressed the drawback of manually designing label graphs in ML-GCN by proposing an adaptive label graph module to learn global label correlations in an end-to-end manner. [PERSON] et al.