Evaluating and optimizing autonomous text classification systems
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Evaluating cost-sensitive Unsolicited Bulk Email categorization
Proceedings of the 2002 ACM symposium on Applied computing
A statistical approach to the spam problem
Linux Journal
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
"In vivo" spam filtering: a challenge problem for KDD
ACM SIGKDD Explorations Newsletter
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
SpamAssassin
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
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Spam Filtering Based On The Analysis Of Text Information Embedded Into Images
The Journal of Machine Learning Research
Spam filtering for short messages
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Email Spam Filtering: A Systematic Review
Foundations and Trends in Information Retrieval
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
Genre-based decomposition of email class noise
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Spam filter evaluation with imprecise ground truth
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Email Accessibility and Social Networking
OCSC '09 Proceedings of the 3d International Conference on Online Communities and Social Computing: Held as Part of HCI International 2009
Filtering spams using the minimum description length principle
Proceedings of the 2010 ACM Symposium on Applied Computing
A Framework for Large-Scale Detection of Web Site Defacements
ACM Transactions on Internet Technology (TOIT)
Online stratified sampling: evaluating classifiers at web-scale
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Rule-based Sam e-mail annotation
RR'10 Proceedings of the Fourth international conference on Web reasoning and rule systems
Social network analysis of web links to eliminate false positives in collaborative anti-spam systems
Journal of Network and Computer Applications
A rule-based system for end-user e-mail annotations
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
Enhanced Topic-based Vector Space Model for semantics-aware spam filtering
Expert Systems with Applications: An International Journal
Text mining and probabilistic language modeling for online review spam detection
ACM Transactions on Management Information Systems (TMIS)
A survey of emerging approaches to spam filtering
ACM Computing Surveys (CSUR)
Content-based mobile spam classification using stylistically motivated features
Pattern Recognition Letters
Facing the spammers: A very effective approach to avoid junk e-mails
Expert Systems with Applications: An International Journal
DPM'11 Proceedings of the 6th international conference, and 4th international conference on Data Privacy Management and Autonomous Spontaneus Security
Security and privacy issues for the network of the future
Security and Communication Networks
Diversity measures for one-class classifier ensembles
Neurocomputing
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Eleven variants of six widely used open-source spam filters are tested on a chronological sequence of 49086 e-mail messages received by an individual from August 2003 through March 2004. Our approach differs from those previously reported in that the test set is large, comprises uncensored raw messages, and is presented to each filter sequentially with incremental feedback. Misclassification rates and Receiver Operating Characteristic Curve measurements are reported, with statistical confidence intervals. Quantitative results indicate that content-based filters can eliminate 98% of spam while incurring 0.1% legitimate email loss. Qualitative results indicate that the risk of loss depends on the nature of the message, and that messages likely to be lost may be those that are less critical. More generally, our methodology has been encapsulated in a free software toolkit, which may used to conduct similar experiments.