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J. D. Sarah and B. Derek, “Textual Case-Based Reasoning for Spam Filtering: A Comparison of feature-Based and Feature-Free Approaches”, Artificial intelligence review, Vol. 26, No. 1-2, 2005, pp.75-87

has been cited by the following article:

• TITLE: Two Approaches on Implementation of CBR and CRM Technologies to the Spam Filtering Problem

  AUTHORS: Rasim Alguliyev, Saadat Nazirova

  KEYWORDS: E-Mail Spam; Unsolicited Bulk Message; Theory of Precedent; CBR; CRM

  JOURNAL NAME: Journal of Information Security, Vol.3 No.1, December 31, 2011

  ABSTRACT: Recently the number of undesirable messages coming to e-mail has strongly increased. As spam has changeable character the anti-spam systems should be trainable and dynamical. The machine learning technology is successfully applied in a filtration of e-mail from undesirable messages for a long time. In this paper it is offered to apply Case Based Reasoning technology to a spam filtering problem. The possibility of continuous updating of spam templates base on the bases of which new coming spam messages are compared, will raise efficiency of a filtration. Changing a combination of conditions it is possible to construct flexible filtration system adapted for different users or corporations. Also in this paper it is considered the second approach as implementation of CRM technology to spam filtration which is not applied to this area yet.