# Application of Label Correlation in Multi-Label Classification: A Survey
## 1 Introduction  
With the rapid growth of internet data, the content contained in data objects is becoming increasingly rich, and the label information is correspondingly becoming more complex. To better understand and analyze data, researchers have conducted studies related to classification tasks. The goal of classification tasks is to train models using training samples of known categories and use the obtained model to predict the categories of unknown samples [1]. Traditional classification methods typically deal with single-label classification problems, where each sample is associated with only one label. However, in practical applications, classification tasks often involve complex data objects, where samples may be assigned multiple labels simultaneously [2]. Based on the number of labels, such data is referred to as multi-label data. Compared to single-label data, multi-label data can more accurately capture the ambiguity of objects in the real world. Methods for classifying multi-label data, known as Multi-Label Classification (MLC) methods, have become a prominent research focus [3]. A straightforward approach to addressing the MLC problem is to treat labels independently, transforming the multi-label problem into a set of binary classification tasks [4], where the presence of each label in a sample is predicted separately. However, this method overlooks the fact that, in real-world multi-label data, labels are not independent of one another but instead exhibit certain relationships [5]. For instance, some labels might frequently co-occur, while others may be mutually exclusive. This interdependence among labels is referred to as label correlations. Label correlations are a crucial characteristic ofMLC. Effective utilization of label correlations can significantly enhance the MLC process and help learn more effective and robust classifiers [6]. If there is a strong dependency between two labels, \(l_{i}\) and \(l_{j}\), instances associated with label \(l_{i}\) are likely also to possess label \(l_{j}\). Therefore, when a classifier predicts that an instance has a certain label, it can also estimate the likelihood of the instance having other strongly correlated labels. Multi-label Data has become increasingly common in modern applications, with major challenges including high-dimensional feature spaces and a large, sparse label space. High-dimensional data often contains complex label correlations and feature redundancy, which further complicates classification, while the sparsity of the label space leads to label imbalance in MLC. Additionally, noisy data in some datasets can severely affect model performance. To address complex label correlations and redundant features resulting from high-dimensional data, [PERSON] et al. [7] proposed a multi-label feature selection method, Learning Correlated and Independent Feature Selection (LCIFS), which simultaneously explores label correlations and regulates feature redundancy. To tackle incomplete label sets, [PERSON] et al. [8] introduced an updated Correlation-Enhanced Feature Learning (uCeFL) approach for MLC. To address label noise, [PERSON] et al. [9] proposed a novel multi-label feature selection method called AGLE. This method leverages adaptive graph learning and label information enhancement to mitigate label noise and has been validated across multiple datasets. In this context, leveraging label correlations can effectively reduce classifier complexity, minimize the number of incorrectly predicted labels, mitigate the impact of noisy labels, and improve classification performance. Additionally, in some large-scale datasets, the sparsity of positive samples for certain labels poses a class imbalance challenge, which can lead to a decline in prediction performance for rare labels during training and generalization. Particularly in Extreme Multi-Label Text Classification (XMTC) tasks, which aim to assign relevant labels to unseen texts from a large-scale label set, challenges arise from the massive number of labels and the inherent complexity of the task.