Variable precision rough set model
Journal of Computer and System Sciences
Performance standards and evaluations in IR test collections: cluster-based retrieval models
Information Processing and Management: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Information Retrieval
Modern Information Retrieval
An Application for Knowledge Discovery Based on a Revision of VPRS Model
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Adaptive anti-spam filtering for agglutinative languages: a special case for Turkish
Pattern Recognition Letters
Applying lazy learning algorithms to tackle concept drift in spam filtering
Expert Systems with Applications: An International Journal
An empirical study of three machine learning methods for spam filtering
Knowledge-Based Systems
SpamHunting: An instance-based reasoning system for spam labelling and filtering
Decision Support Systems
Text classification: A least square support vector machine approach
Applied Soft Computing
Fighting unicode-obfuscated spam
Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit
An Alliance-Based Anti-spam Approach
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
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
Rough Set Approach to Spam Filter Learning
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
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
A collaborative anti-spam system
Expert Systems with Applications: An International Journal
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
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
Artificial immune system based on interval type-2 fuzzy set paradigm
Applied Soft Computing
A survey and experimental evaluation of image spam filtering techniques
Pattern Recognition Letters
Classifying email using variable precision rough set approach
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
A three-way decision approach to email spam filtering
AI'10 Proceedings of the 23rd Canadian conference on Advances 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
Optimising anti-spam filters with evolutionary algorithms
Expert Systems with Applications: An International Journal
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Nowadays, spam represents an extensive subset of the information delivered through Internet involving all unsolicited and disturbing communications received while using different services including e-mail, weblogs and forums. In this context, this paper reviews and brings together previous approaches and novel alternatives for applying rough set (RS) theory to the spam filtering domain by defining three different rule execution schemes: MFD (most frequent decision), LNO (largest number of objects) and LTS (largest total strength). With the goal of correctly assessing the suitability of the proposed algorithms, we specifically address and analyse significant questions for appropriate model validation like corpus selection, preprocessing and representational issues, as well as different specific benchmarking measures. From the experiments carried out using several execution schemes for selecting appropriate decision rules generated by rough sets, we conclude that the proposed algorithms can outperform other well-known anti-spam filtering techniques such as support vector machines (SVM), Adaboost and different types of Bayes classifiers.