# Application of Label Correlation in Multi-Label Classification: A Survey
## 2 Overview of Label Correlation
### Multi-Label Data  
According to the processing of data features, multi-label data can be divided into two categories. First, one category comprises data that has undergone preprocessing and feature engineering. The features of such datasets are already prepared, making them ready for direct input into machine learning algorithms for training and application. Second, the other category consists of raw, unprocessed data, such as text, images, audio, or video in various formats. These datasets typically require feature extraction and are suitable for deep learning models that can automatically learn data features. Table 1 presents several multi-label datasets from various application domains. The Label Cardinality (LC) denotes the average number of labels associated with each instance, as indicated in Equation (1). Here, \(Y_{i}\) denotes the label vector corresponding to instance \(x_{i}\), and \(n\) represents the number of instances in the dataset \(D\)  
\[\text{LC}(D)=\frac{1}{|n|}\sum_{i=1}^{n}\lvert Y_{i}\rvert \tag{1}\]  
In the following, these two types of datasets are introduced, which more or less imply the key feature of label correlations. The CAL500 [17] dataset is a music dataset consisting of 500 songs. It includes a total of 174 music-related labels, with each song annotated with multiple labels. The Yeast [18] dataset represents individual yeast genes, comprising a total of 2,417 yeast genes. Each gene is associated with a group of labels representing gene functions, annotated with one or more of the 14 gene functions. The Medical [19] dataset comprises 978 medical case reports, each labeled with one or more of the 45 disease codes, and is primarily used for text classification tasks. The Delicious [20] dataset contains 16,105 web pages \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Applications** & **Domain** & \begin{tabular}{c} **Instances** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Labels** \\ **Number** \\ \end{tabular} & \
\begin{tabular}{c} **Feature** \\ **Dimension** \\ \end{tabular} & **LC** \\ \hline CAL500 & Audio annotation and retrieval tasks & music & 500 & 174 & 68 & 26.04 \\ Yeast & Multi-Label Classification & biology & 2417 & 14 & 103 & 4.24 \\ Medical & Multi-Label Text Classification & text & 978 & 45 & 1449 & 1.25 \\ Delicious & Clinical Multi-Label Text Classification & text & 16,105 & 983 & 500 & 19.02 \\ Tmc2007 & Text Classification & text & 28,596 & 22 & 500 & 2.16 \\ PubMed & Extreme Multi-Label Text Classification & text & 898,546 & 17,963 & / & 7.80 \\ Vocal Object Classification and & image & 9963 & 20 & / & 1.56 \\ Detection Tasks & & image & 123,287 & 80 & / & 2.90 \\ COCO2014 &
\begin{tabular}{c} Object Recognition and Scene \\ Understanding \\ \end{tabular} & image & 123,287 & 80 & / & 2.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MLC datasets in different domains. of text data along with their labels. The Tmc2007 [21] dataset belongs to the aviation flight safety report dataset, containing 28,596 aviation safety text reports and 22 labels representing flight problem types. The PubMed dataset [22], maintained by the U.S. National Library of Medicine (NLM), contains a vast collection of biomedical literature, covering medical information networks, life science journals, and online books. In addition to providing literature retrieval services, PubMed classifies and indexes documents using MeSH (Medical Subject Headings), a vocabulary of over 26,000 medical terms organized in a hierarchical structure. Another type of dataset suitable for deep learning multi-label tasks includes Pascal VOC2007 [23] and MS COCO2014 [24]. The Pascal VOC dataset is primarily used for object detection, image classification, and semantic segmentation tasks. It contains a total of 9963 images, with 5011 images in the training and validation sets and 4952 images in the test set, covering 20 label categories.