文章基本信息

标题：A Systematic Literature Review on Multi-Label Classification based on Machine Learning Algorithms
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作者：Nurshahira Endut ; W. M. Amir Fazamin W. Hamzah ; Ismahafezi Ismail 等
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.