Comparison of feature selection methods in Kurdish text classification-Reference-Cited by-同舟云学术

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Comparison of feature selection methods in Kurdish text classification

Published:2023-08-29 Issue: Volume: Page:
ISSN:2520-8438
Container-title:Iran Journal of Computer Science
language:en
Short-container-title:Iran J Comput Sci

Author:

Saeed Ari M.,Badawi Soran,Ahmed Sara A.,Hassan Diyari A.

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s42044-023-00159-4.pdf

Reference30 articles.

1. Parlak, B., Uysal, A.K.: A novel filter feature selection method for text classification: extensive feature selector. J Inf Sci (2021). https://doi.org/10.1177/0165551521991037

2. Onan, A.: An ensemble scheme based on language function analysis and feature engineering for text genre classification. J Inf Sci 44(1), 28–47 (2018). https://doi.org/10.1177/0165551516677911

3. Amir Sjarif, N.N., Mohd Azmi, N.F., Chuprat, S., Sarkan, H.M., Yahya, Y., Sam, S.M.: SMS spam message detection using term frequency-inverse document frequency and random forest algorithm. Procedia Comput Sci 161, 509–515 (2019). https://doi.org/10.1016/j.procs.2019.11.150

4. Gupta, S. T., Sahoo, J. K., Roul, R. K.: Authorship identification using recurrent neural networks. In: Proceedings of the 2019 3rd International Conference on Information System and Data Mining—ICISDM 2019, pp. 133–137 (2019). https://doi.org/10.1145/3325917.3325935

5. Chang, Y.-C., Hsieh, Y.-L., Chen, C.-C., Hsu, W.-L.: A semantic frame-based intelligent agent for topic detection. Soft comput 21(2), 391–401 (2017). https://doi.org/10.1007/s00500-015-1695-4

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