Original Contribution: Stacked generalization
Neural Networks
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
Identifying Junk Electronic Mail in Microsoft Outlook with a Support Vector Machine
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
A Neural Network Based Approach to Automated E-Mail Classification
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
An empirical study of spam traffic and the use of DNS black lists
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Adapting Bayesian statistical spam filters to the server side
Journal of Computing Sciences in Colleges
A comparison of event models for Naive Bayes anti-spam e-mail filtering
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
GMDH-based feature ranking and selection for improved classification of medical data
Journal of Biomedical Informatics
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
An HMM for detecting spam mail
Expert Systems with Applications: An International Journal
An empirical study of three machine learning methods for spam filtering
Knowledge-Based Systems
Workload models of spam and legitimate e-mails
Performance Evaluation
An incremental cluster-based approach to spam filtering
Expert Systems with Applications: An International Journal
Construction and analysis of educational tests using abductive machine learning
Computers & Education
Introduction to Information Retrieval
Introduction to Information Retrieval
On the properties of spam-advertised URL addresses
Journal of Network and Computer Applications
A fuzzy similarity approach for automated spam filtering
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Obtaining the threat model for e-mail phishing
Applied Soft Computing
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Unsolicited or spam email has recently become a major threat that can negatively impact the usability of electronic mail. Spam substantially wastes time and money for business users and network administrators, consumes network bandwidth and storage space, and slows down email servers. In addition, it provides a medium for distributing harmful code and/or offensive content. In this paper, we explore the application of the GMDH (Group Method of Data Handling) based inductive learning approach in detecting spam messages by automatically identifying content features that effectively distinguish spam from legitimate emails. We study the performance for various network model complexities using spambase, a publicly available benchmark dataset. Results reveal that classification accuracies of 91.7% can be achieved using only 10 out of the available 57 attributes, selected through abductive learning as the most effective feature subset (i.e. 82.5% data reduction). We also show how to improve classification performance using abductive network ensembles (committees) trained on different subsets of the training data. Comparison with other techniques such as neural networks and naive Bayesian classifiers shows that the GMDH-based learning approach can provide better spam detection accuracy with false-positive rates as low as 4.3% and yet requires shorter training time.