@inproceedings{94d9e05d97f84d6486c531e40a632249,
title = "A supervised approach for multi-label classification of Arabic news articles",
abstract = "Multi-label classification of textual data is an important problem with the growing size of available data and the increasing difficulties in assigning a single label to each piece of text. Examples range from news articles to emails. Most of the existing works consider English text. This work focuses on multi-label classification of Arabic articles. After dataset collection, three multi-label classifiers are considered (DT, RF and KNN). The results show a superiority of DT over the other two classifiers.",
keywords = "Decision Tree, K-Nearest Neighbors, Multi-label classification, Random Forest",
author = "Shehab, \{Mohammed A.\} and Omar Badarneh and Mahmoud Al-Ayyoub and Yaser Jararweh",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 7th International Conference on Computer Science and Information Technology, CSIT 2016 ; Conference date: 13-07-2016 Through 14-07-2016",
year = "2016",
month = aug,
day = "23",
doi = "10.1109/CSIT.2016.7549465",
language = "English",
series = "Proceedings - CSIT 2016: 2016 7th International Conference on Computer Science and Information Technology",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - CSIT 2016",
address = "United States",
}