The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Boosting a weak learning algorithm by majority
Information and Computation
Performance standards and evaluations in IR test collections: cluster-based retrieval models
Information Processing and Management: an International Journal
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Understanding PKI: Concepts, Standards, and Deployment Considerations
Understanding PKI: Concepts, Standards, and Deployment Considerations
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
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
SpamHunting: An instance-based reasoning system for spam labelling and filtering
Decision Support Systems
Experimental perspectives on learning from imbalanced data
Proceedings of the 24th international conference on Machine learning
Rough Set Approach to Spam Filter Learning
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Assessing Classification Accuracy in the Revision Stage of a CBR Spam Filtering System
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Classifying email using variable precision rough set approach
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
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
Effective scheduling strategies for boosting performance on rule-based spam filtering frameworks
Journal of Systems and Software
Hybrid email spam detection model with negative selection algorithm and differential evolution
Engineering Applications of Artificial Intelligence
Hi-index | 12.05 |
Tragedy of Commons Theory introduced by Hardin (1968) revealed how shared and limited resources get completely depleted as effect of human behaviour. By analogy, common spamming activities can be properly modelled by this solid theory and, consequently, a young Internet Security Industry has recently emerged to fight against spam. However, the massive intensification of spam deliveries during last years has led to the need of achieving a significant improvement in filter accuracy. In this context, current research efforts are mainly focussed on providing a wide variety of content-based techniques able to overcome common spam filtering inconveniencies. Although theoretical filtering evaluation is generally taken into consideration in scientific works, most of the evaluation protocols are not appropriate to correctly assess the performance of models during filter operation in real environments. In order to cover the gap between basic research and applied deployment of well-known spam filtering techniques, this work proposes a novel straightforward evaluation methodology able to rank available models using four different but complementary perspectives: static, dynamic, adaptive and internationalisation. In the present study, we applied our SDAI methodology to compare eight different well-known content-based spam filtering techniques using several established accuracy measures. Results showed the effect of the knowledge grain-size and evidenced several unexpected situations related with the behaviour of analysed models.