# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
###### Abstract  
Over the decades, a tremendous increase has been witnessed in the production of documents available in digital form. The increased production of documents has gained so much momentum that their rate of production jumps two-fold every five years. These articles are searched over the internet via search engines, digital libraries, and citation indexes. However, the retrieval of relevant research papers for user queries is still a pipedream. This is because scientific documents are not indexed based on some subject classification hierarchies. Hence, the classification of these documents becomes a challenging task for the researchers. Classification of the documents can be two-fold: one way is to assign a single label to each document and the other is to assign multi-labels to each document based on its belonging domains. Classification of the documents can be performed by using either the available metadata or the whole content of the documents. While performing classification, there are many challenges which may belong to the dataset, feature selection technique, preprocessing methodology, and which classification model is suitable for the classification of the documents. This paper highlights the issues for single-label and multi-label classification by using either metadata or content of the documents and why metadata-based approaches are better than content-based approaches in terms of feasibility. classification; single label; multi-label; data mining and ML; digital libraries 20232002333 The vast number of these documents is unstructured in nature, due to which search systems are not efficient enough to retrieve the most relevant documents [6]. When the user poses a query, the search systems return a bulk of documents from which very few documents hold relevance to the query. Because of this disorganization of research publications, the problem of classifying research articles into the appropriate category has gained the attention of a lot of researchers in the document classification community. The researchers aimed to classify the research articles in such a way that guarantees maximum relevant information retrieval [7]. The availability of this huge corpus on the digital web has made it challenging for researchers to classify the publications into various categories. In machine learning (ML), classification is regarded as a central concept that aims to classify items into two or more groups. The classification is performed on various ML problems, for instance, speech recognition [8], text categorization [9; 10], etc. In scientific literature, document classification is beneficial to retrieve useful information [11]. The usual method of document classification comprises the selection of useful features from the data that could help to assign some target category. The classification can be of two forms: (1) single-label classification (i.e., classifying the items into a single class) and (2) multi-label classification (i.e., classifying the items into more than one class), since a research article can have an association with multiple categories. Therefore, multi-label classification has gained the attention of many researchers who have classified research articles into multiple categories [12; 13]. Most of the multi-label classification schemes are of low accuracy and classify research articles into a limited number of categories [14; 15; 16]. The classification of research articles into multiple categories with high accuracy is a challenging task [17]. Of course, multi-label classification requires an immense effort to produce a diversified and comprehensive set of features that specifically belong to each category. This research work specifically focuses on the multi-label classification of research articles with good accuracy to overcome the existing gap. How to automatically assign an appropriate category to the document or research article? In the late 1980s, document classification was performed by manually building human-crafted rules for assigning a document to some predefined category.