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

TEM Journal

关键词: multi-label;    classification;    machine learning;   

DOI  :  10.18421/TEM112-20

来源: DOAJ

【 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.

【 授权许可】

Unknown