# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches
### Metadata-Based Approaches  
The contemporary metadata-based state-of-the-art research articles' classification schemes exploit the metadata of research articles for their classification into a pre-defined hierarchy. Metadata of scientific documents include title, authors, keywords, categories, funding/acknowledgement, references section, etc. These forms of metadata are almost freely available online as compared to the whole content of the articles. This section focuses on a brief overview of the metadata-based approaches.  
[PERSON] proposed a metadata extraction scheme [42] for document classification. This was a \"post hoc\" classification system for document classification. After the metadata extraction of the document, the post hoc technique applied further to classify these documents. [PERSON] and [PERSON] [16] proposed a framework by using the Bayesian Network (BN) for the classification of conference papers. They used the keywords of research papers for classification. A feature selection algorithm is applied to automatically extracted keywords for each topic. To improve the performance of document classification approaches into predefined categories, [PERSON] et al. proposed another approach [43]. In this approach, they combined citation information and structural contents such as the title and abstract of the documents. Different similarity measures based on the structural contents and citation information are evaluated to improve the effectiveness of the classification. To address the document classification problem, the researchers employed different schemes on two data sources such as metadata and content.  
In 2023, [PERSON] et al. [50] proposed a novel metadata-based approach for the classification of computer science published articles. Two diverse datasets were investigated in this regard. First, the dataset was obtained from the Journal of Universal Computer Science (J.UCS) and the second benchmark dataset was obtained from the Association for Computing Machinery (ACM) published articles [51]. The proposed approach was able to classify the articles based on metadata only and the performance was comparable to that of the content-based approaches in the literature.  
The content-based schemes exploit the content of research articles for their classification [12; 13; 14; 15; 16; 24; 27; 30; 36; 40; 43; 44]. Every scheme has its own pros and cons which depend on the size, pre-processing, and nature of the dataset. For these schemes' implementation, the content of research articles is an essential requirement. The content-based schemes provide better precision due to the rich number of features [13]; however, the content of scientific documents is not freely available most of the time. On the other hand, very few researchers have used only the metadata of the documents for the classification [16; 42; 43]. The metadata of the documents provide a limited number of features which may result in low accuracy as compared to the content-based document classification schemes. The objective of this paper is to use freely available metadata and to analyze to what extent the metadata-based features can behave like content-based features. Moreover, to what extent is the scheme effective for the sake of multi-label classification? The metadata are freely available in most scientific digital libraries such as IEEE ([[http://ieeexplore.ieee.org/](http://ieeexplore.ieee.org/)]([http://ieeexplore.ieee.org/](http://ieeexplore.ieee.org/)) (accessed on 15 January 2023)), ACM ([[http://dl.acm.org/citation.cfm?id=2077531](http://dl.acm.org/citation.cfm?id=2077531)]([http://dl.acm.org/citation.cfm?id=2077531](http://dl.acm.org/citation.cfm?id=2077531)) (accessed on 15 January 2023)), and Springer ([[http://link.springer.com/chapter/10.1007%2F11925231_98](http://link.springer.com/chapter/10.1007%2F11925231_98)]([http://link.springer.com/chapter/10.1007%2F11925231_98](http://link.springer.com/chapter/10.1007%2F11925231_98)) (20 January 2023)).