Predatory journal classification using machine learning

Li Xian Chen*, Kai Sin Wong, Chia Hung Liao, Shyan Ming Yuan

*此作品的通信作者

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

The prevalence of predatory journals has become more severe recently as this is harmful to science and technology development. For scholars publish papers more effectively and avoid publishers for profits, this research used a machine learning method to identify the predatory journals. The features like text content and keywords of the collected journals' websites were extracted from mainstream predatory journal websites and normal journal websites. This research proposed a predatory journal classification system based on a new model. The results show that our model's recall rate exceeds 90%, ensuring that the journals submitted by the researchers are not predatory.

原文English
主出版物標題Proceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020
編輯Teen-Hang Meen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面193-196
頁數4
ISBN(電子)9781728193335
DOIs
出版狀態Published - 21 8月 2020
事件3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020 - Kaohsiung, Taiwan
持續時間: 21 8月 202023 8月 2020

出版系列

名字Proceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020

Conference

Conference3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020
國家/地區Taiwan
城市Kaohsiung
期間21/08/2023/08/20

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