C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Modern Information Retrieval
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Using latent semantic indexing to filter spam
Proceedings of the 2003 ACM symposium on Applied computing
Combining text and heuristics for cost-sensitive spam filtering
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Applying lazy learning algorithms to tackle concept drift in spam filtering
Expert Systems with Applications: An International Journal
SpamHunting: An instance-based reasoning system for spam labelling and filtering
Decision Support Systems
DMTP: Controlling spam through message delivery differentiation
Computer Networks: The International Journal of Computer and Telecommunications Networking
Analyzing the Performance of Spam Filtering Methods When Dimensionality of Input Vector Changes
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A Comparative Impact Study of Attribute Selection Techniques on Naïve Bayes Spam Filters
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
The Impact of Noise in Spam Filtering: A Case Study
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Managing irrelevant knowledge in CBR models for unsolicited e-mail classification
Expert Systems with Applications: An International Journal
A collaborative anti-spam system
Expert Systems with Applications: An International Journal
Targeting spam control on middleboxes: Spam detection based on layer-3 e-mail content classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
Using phrases as features in email classification
Journal of Systems and Software
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
A scalable intelligent non-content-based spam-filtering framework
Expert Systems with Applications: An International Journal
Enhanced email spam filtering through combining similarity graphs
Proceedings of the fourth ACM international conference on Web search and data mining
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A comparative performance study of feature selection methods for the anti-spam filtering domain
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Effective scheduling strategies for boosting performance on rule-based spam filtering frameworks
Journal of Systems and Software
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Resulting from the huge expansion of Internet usage, the problem of unsolicited commercial e-mail (UCE) has grown astronomically. Although a good number of successful content-based anti-spam filters are available, their current utilization in real scenarios is still a long way off. In this context, the SpamAssassin filter offers a rule-based framework that can be easily used as a powerful integration and deployment tool for the fast development of new anti-spam strategies. This paper presents Grindstone4Spam, a publicly available optimization toolkit for boosting SpamAssassin performance. Its applicability has been verified by comparing its results with those obtained by the default SpamAssassin software as well as four well-known anti-spam filtering techniques such as Naive Bayes, Flexible Bayes, Adaboost and Support Vector Machines in two different case studies. The performance of the proposed alternative clearly outperforms existing approaches working in a cost-sensitive scenario.