Open Access

ARTICLE

A Novel Metadata Based Multi-Label Document Classification Technique

by Naseer Ahmed Sajid¹, Munir Ahmad¹, Atta-ur Rahman^2,*, Gohar Zaman³, Mohammed Salih Ahmed⁴, Nehad Ibrahim², Mohammed Imran B. Ahmed⁴, Gomathi Krishnasamy⁶, Reem Alzaher², Mariam Alkharraa², Dania AlKhulaifi², Maryam AlQahtani², Asiya A. Salam⁶, Linah Saraireh⁵, Mohammed Gollapalli⁶, Rashad Ahmed⁷

1 Barani Institute of Information Technology (BIIT), PMAS Arid Agriculture University, Rawalpindi, 46000, Punjab, Pakistan
2 Department of Computer Science (CS), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
3 Faculty Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
4 Department of Computer Engineering (CE), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
5 College of Business Administration, Imam Abdulrahman bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
6 Department of Computer Information System (CIS), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
7 ICS Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

* Corresponding Author: Atta-ur Rahman. Email:

Computer Systems Science and Engineering 2023, 46(2), 2195-2214. https://doi.org/10.32604/csse.2023.033844

Received 29 June 2022; Accepted 13 November 2022; Issue published 09 February 2023

Abstract

From the beginning, the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies, its growth rate is overwhelming. On a rough estimate, more than thirty thousand research journals have been issuing around four million papers annually on average. Search engines, indexing services, and digital libraries have been searching for such publications over the web. Nevertheless, getting the most relevant articles against the user requests is yet a fantasy. It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification. To overcome this issue, researchers are striving to investigate new techniques for the classification of the research articles especially, when the complete article text is not available (a case of non-open access articles). The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess, “to what extent metadata-based features can perform in contrast to content-based approaches.” In this regard, novel techniques for investigating multilabel classification have been proposed, developed, and evaluated on metadata such as the Title and Keywords of the articles. The proposed technique has been assessed for two diverse datasets, namely, from the Journal of universal computer science (J.UCS) and the benchmark dataset comprises of the articles published by the Association for computing machinery (ACM). The proposed technique yields encouraging results in contrast to the state-of-the-art techniques in the literature.

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Keywords

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