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  • 标题:A Systematic Literature Review on Multi-Label Classification based on Machine Learning Algorithms
  • 本地全文:下载
  • 作者:Nurshahira Endut ; W. M. Amir Fazamin W. Hamzah ; Ismahafezi Ismail
  • 期刊名称:TEM Journal
  • 印刷版ISSN:2217-8309
  • 电子版ISSN:2217-8333
  • 出版年度:2022
  • 卷号:11
  • 期号:2
  • 页码:658-666
  • DOI:10.18421/TEM112-20
  • 语种:English
  • 出版社:UIKTEN
  • 摘要: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.
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