# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 3 The Issues and Challenges and Insight of Contemporary Classification Approaches  
The following are the issues or challenges which were observed from the above discussion in Section 2. The existing research articles' classification schemes depend upon the content of the articles. In this context, most of the time, the non-availability of research articles (complete research paper) makes those schemes non-applicable. There is a need for some best alternative way to classify research articles that produce results closer or better than content-based approaches. Most state-of-the-art approaches focus on single-label classification, while research articles may belong to multiple categories. There is a need for such a multi-label classification system that utilizes the best possible alternate of the content-based approaches with closer or improved accuracy. The existing multi-label classification schemes classify citations into a limited number of categories. While a research article may belong to multiple categories, for instance, in the computer science domain, the research articles may belong to more than one category of ACM classification system. The ACM categorization system has 11 topics on its root level. There is a need for an approach that is efficient enough to classify research articles at least to the root level of the ACM classification system. There are also other challenges which were observed and can make sophisticated problems for the researcher who is working in the domain of the text classification by using either the content or metadata of the research papers. Feature selection is an important part of text classification, feature vectors must have some meaning which represents the text, and these features must be free of noise. Due to the presence of outliers and unknown classes, the classification of the text may become more subjective. According to the range of text, features from the text may vary from hundreds of thousands to thousands of features. An accurate feature selection can significantly yield a great contract of mileage in the text classification process. The content of the research papers may yield a large amount of data; stemming may turn down the performance of the classifiers. There are very few state-of-the-art approaches that rely on freely available metadata as shown in Table 3. These schemes classify documents into single label [16; 42; 43]. Only the approaches proposed by [PERSON] and [PERSON] [36], [PERSON] [37], and [PERSON] and [PERSON] [38] classify documents to multiple classes but by exploiting the content of the documents. All other approaches have not dealt with the multi-label classification problem. The existing multi-label classification schemes classify documents into a limited number of categories. The researchers who used the metadata of the papers only performed the single-label classification and achieved up to almost 0.84 accuracy by using a few numbers of classes. Deep learning-based approaches have become more popular in terms of better performance. For instance, according to Table 4, these schemes exhibit an average accuracy of 90% and above [62; 64; 65; 66] with multi-label classification. However, again, the dependency is mainly on the availability of the contents. Nonetheless, when it comes to the limited metadata of the research articles, the accuracy and other measures are seriously degraded, as seen in the case of [61]. Document classification is more challenging than that of other text classifications such as tweets, reviews, news articles, etc. That is mainly because of the following:  
1. Research articles are subject to open-access issues. Non-open-access journal articles do not provide the content of the paper but only the metadata. 2. Research articles are mainly available in PDF format. Parsing the PDF document and converting it into text, especially when the document is not structured and/or requires optical character recognition (OCR), mainly results in textual errors that eventually may affect the classification accuracy of the employed model.