@inproceedings{81c2d5a59e834c42b90f71a081b0f970,
title = "Empirical Study of Filtered-Based Feature Selection Methods for Arabic Text Classification",
abstract = "Text classification has many applications in various fields; such as news categorization, sentiment analysis, E-mail spam filtering, and others. However, handling textual data is a challenging task owing to the potentially massive number of features (words). The presence of redundant irrelevant features deteriorates the performance of a learning algorithm and makes the process of text classification more complex. This research conducts a comparison study of several filtering-based feature se-lection methods in the context of Arabic text classification. Arabic is a highly complex language syntactically and morphologically which leads to more complicated learning tasks. Proposing a ro-bust classification model is demanding. Remarkably, integrating filtering approaches results in significant improvements in the performance of classification algorithms.",
keywords = "Chi Square, Feature Selection, Maximum Features per Document, Maximum Features per Document Reduced, Mutual Information, Text classification",
author = "Fatima Shannaq and Maria Habib and Hossam Faris and Hammad, \{Mahmoud M.\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 6th World Symposium on Communication Engineering, WSCE 2023 ; Conference date: 27-09-2023 Through 29-09-2023",
year = "2023",
doi = "10.1109/WSCE59557.2023.10365965",
language = "English",
series = "2023 6th World Symposium on Communication Engineering, WSCE 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "114--119",
booktitle = "2023 6th World Symposium on Communication Engineering, WSCE 2023",
address = "United States",
}