Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks
Author:
Funder
Student Grant Competition
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
http://link.springer.com/article/10.1007/s10489-018-1161-y/fulltext.html
Reference87 articles.
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2. Ahmed I, Ali R, Guan D, Lee YK, Lee S, Chung T (2015) Semi-supervised learning using frequent itemset and ensemble learning for SMS classification. Expert Syst Appl 42(3):1065–1073. https://doi.org/10.1016/j.eswa.2014.08.054
3. Almeida TA, Almeida J, Yamakami A (2011) Spam filtering: how the dimensionality reduction affects the accuracy of Naive Bayes classifiers. J Internet Serv Appl 1(3):183–200. https://doi.org/10.1007/s13174-010-0014-7
4. Almeida TA, Hidalgo JMG, Yamakami A (2011) Contributions to the study of SMS spam filtering: new collection and results. In: Proceedings of the 11th ACM symposium on document engineering, pp 259–262. https://doi.org/10.1145/2034691.2034742
5. Almeida TA, Yamakami A (2012) Occam’s razor-based spam filter. J Internet Serv Appl 3(3):245–253. https://doi.org/10.1007/s13174-012-0067-x
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