@inproceedings{6ae7971d3d89443284a57b54e68405a5,
title = "Predatory journal classification using machine learning",
abstract = "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.",
keywords = "Classifier, Machine learning, Predatory journals",
author = "Chen, {Li Xian} and Wong, {Kai Sin} and Liao, {Chia Hung} and Yuan, {Shyan Ming}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020 ; Conference date: 21-08-2020 Through 23-08-2020",
year = "2020",
month = aug,
day = "21",
doi = "10.1109/ICKII50300.2020.9318901",
language = "English",
series = "Proceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "193--196",
editor = "Teen-Hang Meen",
booktitle = "Proceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020",
address = "美國",
}