Feature selection methods for text classification: a systematic literature review
Author:
Funder
CNPq-Brazil
FAPERJ
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Linguistics and Language,Language and Linguistics
Link
https://link.springer.com/content/pdf/10.1007/s10462-021-09970-6.pdf
Reference214 articles.
1. Abdollahi M, Gao X, Mei Y, Ghosh S, Li J (2019) An ontology-based two-stage approach to medical text classification with feature selection by particle swarm optimisation. In: Proceedings of the IEEE congress on evolutionary computation, pp 119–126
2. Agnihotri D, Verma K, Tripathi P (2017) Variable global feature selection scheme for automatic classification of text documents. Expert Syst Appl 81:268–281. https://doi.org/10.1016/j.eswa.2017.03.057
3. Agnihotri D, Verma K, Tripathi P (2016) Computing correlative association of terms for automatic classification of text documents. Proceedings of the international symposium on computer vision and the internet, https://doi.org/10.1145/2983402.2983424
4. Agnihotri D, Verma K, Tripathi P (2017a) Mutual information using sample variance for text feature selection. In: Proceedings of the international conference on communication and information processing, pp 39–44, https://doi.org/10.1145/3162957.3163054
5. Agnihotri D, Verma K, Tripathi P, Singh B (2018) Soft voting technique to improve the performance of global filter based feature selection in text corpus. Appl Intell 49. https://doi.org/10.1007/s10489-018-1349-1
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