The Performance of Logistic Regression and Discriminant Analysis in Spam E-mail Classification-Reference-Cited by-同舟云学术

The Performance of Logistic Regression and Discriminant Analysis in Spam E-mail Classification

Published:2022 Issue: Volume: Page:467-478
ISSN:2198-4182
Container-title:Studies in Systems, Decision and Control
language:
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Author:

Hao Lim Ying,Awang Norhashidah

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-04028-3_30

Reference27 articles.

1. Guzella, T.S., Caminhas, W.M.: A review of machine learning approaches to spam filtering. Exp. Syst. Appl. 36(7), 10206–10222 (2009)

2. Cohen, W.W.: Learning rules that classify e-mail. In: AAAI Spring Symposium on Machine Learning in Information Access, 25–27 March 1996. Palo Alto (1996)

3. Harisinghaney, A., Dixit, A., Gupta, S., Arora, A.: Text and image based spam e-mail classification using KNN, Naïve Bayes and reverse DBSCAN algorithm. In: ICROIT 2014—International Conference on Reliability Optimization and Information Technology, 6–8 February 2014. Faridabad, India (2014)

4. Erickson, D., Casado, M., McKeown. N.: The effectiveness of whitelisting: a user-study. In: CEAS 2008-The Fifth Conference on E-mail and Anti-Spam, 21–22 August 2008, Mountain View, California (2008)

5. Levine, J.R.: Experiences with Greylisting. In: CEAS 2005—Second Conference on E-mail and Anti-Spam, 21–22 July 2005. Stanford University (2005)

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