Averaging over decision stumps
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
The State of the Art in Text Filtering
User Modeling and User-Adapted Interaction
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Diagnosis and Decision Support
Case-Based Reasoning Technology, From Foundations to Applications
SpamHunting: An instance-based reasoning system for spam labelling and filtering
Decision Support Systems
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
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
Catching the Drift: Using Feature-Free Case-Based Reasoning for Spam Filtering
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Managing irrelevant knowledge in CBR models for unsolicited e-mail classification
Expert Systems with Applications: An International Journal
Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks
Expert Systems with Applications: An International Journal
Relaxing feature selection in spam filtering by using case-based reasoning systems
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
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In this paper we propose a novel feature selection method able to handle concept drift problems in spam filtering domain. The proposed technique is applied to a previous successful instance-based reasoning e-mail filtering system called SpamHunting. Our achieved information criterion is based on several ideas extracted from the well-known information measure introduced by Shannon. We show how results obtained by our previous system in combination with the improved feature selection method outperforms classical machine learning techniques and other well-known lazy learning approaches. In order to evaluate the performance of all the analysed models, we employ two different corpus and six well-known metrics in various scenarios.