Vol.11, No.2, May 2022.                                                                                                                                                                                   ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

A Systematic Literature Review on Multi-Label Classification based on Machine Learning Algorithms


Nurshahira Endut, W. M. Amir Fazamin W. Hamzah, Ismahafezi Ismail, Mohd Kamir Yusof, Yousef Abu Baker, Hafiz Yusoff


© 2022 W. M. Amir Fazamin W. Hamzah, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 11, Issue 2, Pages 658-666, ISSN 2217-8309, DOI: 10.18421/TEM112-20, May 2022.


Received: 16 January 2022.

Revised:   16 April 2022.
Accepted: 21 April 2022.
Published: 27 May 2022.




Multi-label classification is a technique used for mapping data from single labels to multiple labels. These multiple labels stand part of the same label set comprising inconsistent labels. The objective of multi-label classification is to create a classification model for previously unidentified samples. The accuracy of multi-label classification based on machine learning algorithms has been a particular study and discussion topic for researchers. This research aims to present a systematic literature review on multi-label classification based on machine learning algorithms. This study also discusses machine learning algorithm techniques and methods for multi-label classification. The findings would help researchers to explore and find the best accuracy of multi-label classification. The review result considered the Support Vector Machine (SVM) as the most accurate and appropriate machine learning algorithm in multi-label classification.


Keywords –multi-label, classification, machine learning.



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