Email feedback: a policy-based approach to overcoming false positives
Proceedings of the 2005 ACM workshop on Formal methods in security engineering
The challenges of service-side personalized spam filtering: scalability and beyond
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Spam Filter Based Approach for Finding Fault-Prone Software Modules
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Training on errors experiment to detect fault-prone software modules by spam filter
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Prediction of Fault-Prone Software Modules Using a Generic Text Discriminator
IEICE - Transactions on Information and Systems
Can faulty modules be predicted by warning messages of static code analyzer?
Advances in Software Engineering - Special issue on Software Quality Assurance Methodologies and Techniques
Genetic optimized artificial immune system in spam detection: a review and a model
Artificial Intelligence Review
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In this paper we present a Markov Random Field model based approach to filter spam. Our approach examines the importance of the neighborhood relationship (MRF cliques) among words in an email message for the purpose of spam classification. We propose and test several different theoretical bases for weighting schemes among corresponding neighborhood windows. Our results demonstrate that unexpected side effects depending on the neighborhood window size may have larger accuracy impact than the neighborhood relationship effects of the Markov Random Field.