Introduction to Bayesian Networks
Introduction to Bayesian Networks
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
MailRank: using ranking for spam detection
Proceedings of the 14th ACM international conference on Information and knowledge management
Artificial immune system inspired behavior-based anti-spam filter
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Web intelligence and change discovery
SpamHunting: An instance-based reasoning system for spam labelling and filtering
Decision Support Systems
ECUE: A Spam Filter that Uses Machine Learning to Track Concept Drift
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
An immunological filter for spam
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
An intraday trading model based on Artificial Immune Systems
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Hybrid email spam detection model with negative selection algorithm and differential evolution
Engineering Applications of Artificial Intelligence
| Hi-index | 0.01 |
This paper proposes a novel solution to spam detection inspired by a model of the adaptive immune system known as the cross-regulation model. We report on the testing of a preliminary algorithm on six e-mail corpora. We also compare our results statically and dynamically with those obtained by the Naive Bayes classifier and another binary classification method we developed previously for biomedical text-mining applications. We show that the cross-regulation model is competitive against those and thus promising as a bio-inspired algorithm for spam detection in particular, and binary classification in general.