Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating cost-sensitive Unsolicited Bulk Email categorization
Proceedings of the 2002 ACM symposium on Applied computing
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Tree induction vs. logistic regression: a learning-curve analysis
The Journal of Machine Learning Research
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
Combining winnow and orthogonal sparse bigrams for incremental spam filtering
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Applying lazy learning algorithms to tackle concept drift in spam filtering
Expert Systems with Applications: An International Journal
Online supervised spam filter evaluation
ACM Transactions on Information Systems (TOIS)
Spam Filtering Using Statistical Data Compression Models
The Journal of Machine Learning Research
An incremental cluster-based approach to spam filtering
Expert Systems with Applications: An International Journal
Email Spam Filtering: A Systematic Review
Foundations and Trends in Information Retrieval
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
Evaluation of Approaches for Dimensionality Reduction Applied with Naive Bayes Anti-Spam Filters
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Probabilistic anti-spam filtering with dimensionality reduction
Proceedings of the 2010 ACM Symposium on Applied Computing
Filtering spams using the minimum description length principle
Proceedings of the 2010 ACM Symposium on Applied Computing
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
| Hi-index | 12.06 |
Spam has become an increasingly important problem with a big economic impact in society. Spam filtering poses a special problem in text categorization, in which the defining characteristic is that filters face an active adversary, which constantly attempts to evade filtering. In this paper, we present a novel approach to spam filtering based on the minimum description length principle and confidence factors. The proposed model is fast to construct and incrementally updateable. Furthermore, we have conducted an empirical experiment using three well-known, large and public e-mail databases. The results indicate that the proposed classifier outperforms the state-of-the-art spam filters.