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.

Blockchain Empowered Interoperable Framework for Smart Healthcare

“…In that research, we suggested an electronic data exchange (EDI) framework for smart healthcare in the context of smart cities supported by blockchain technology. To enable secure and effective EHR maintenance and exchange among multiple entities in the smart healthcare ecosystem, the proposed platform makes use of the benefits of blockchain technology [56][57][58][59][60]. Four essential parts made up the proposed framework: (1) Personal servers are local storage devices that store and encrypt EHR and communicate with cloud servers.…”

Predicting Global Energy Consumption Through Data Mining Techniques

“…This section provides the statistical analysis of the dataset which will eventually be helpful during the modelling, designing and analysis phase [33][34][35][36][37]. That mainly includes:…”

Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management

“…Furthermore, the loss rate stands for the total errors or the variance between the predicted and the actual values. Using a variety of metrics will significantly help in defining the performance of the proposed study [51][52][53][54][55][56]. The Equations (1)-( 4) of the previously mentioned measures are seen below.…”