Feature Selection Based on Term Frequency for Arabic Text Classification Using Multilayer Perceptron
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-06971-0_8
Reference11 articles.
1. Al Qadi, L., El Rifai, H., Obaid, S., Elnagar, A.: Arabic text classification of news articles using classical supervised classifiers. University of Sharjah. 2nd International Conference on new Trends in Computing Sciences (ICTCS) , (2019)
2. Alhaj, Y.A., Xiang, J., Zhao, D., Al-Qaness, A.A., Al-Qaness, Abdelaziz, M., Dahou, A.: A study of the effects of stemming strategies on Arabic document classification. IEEE Access. 7, 32664–32671 (2019). https://doi.org/10.1109/ACCESS.2019.2903331
3. Al-Radaideh, Q.: Applications of mining Arabic text: a review chapter. In: Sadollah, A., Sinha, T.S. (eds.) Recent Trends in Computational Intelligence. IntechOpen (2020). https://doi.org/10.5772/intechopen.91275
4. Alsaleem, S.: Automated Arabic text categorization using SVM and NB. Int. Arab J. e-Technol. 2(2) (2011)
5. Boukil, S., El Adnani, F., El Moutaouakkil, A., Cherrat, L., Ezziyyani, M.: Arabic stemming techniques as feature extraction applied in Arabic text classification. Adv. Inf. Technol. Serv. Syst. Lect. Note Netw. Syst. 25 (2018). https://doi.org/10.1007/978-3-319-69137-4_31
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