Instance-Based Learning Algorithms
Machine Learning
Generalizing from case studies: a case study
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning in the presence of concept drift and hidden contexts
Machine Learning
Tolerating Concept and Sampling Shift in Lazy Learning UsingPrediction Error Context Switching
Artificial Intelligence Review - Special issue on lazy learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Adapting to Drift in Continuous Domains (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Fish and Shrink. A Next Step Towards Efficient Case Retrieval in Large-Scale Case Bases
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Competence-Guided Case-Base Editing Techniques
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Diagnosis and Decision Support
Case-Based Reasoning Technology, From Foundations to Applications
Text Categorization Using Compression Models
DCC '00 Proceedings of the Conference on Data Compression
DNA Sequence Classification Using Compression-Based Induction
DNA Sequence Classification Using Compression-Based Induction
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An Assessment of Case-Based Reasoning for Spam Filtering
Artificial Intelligence Review
Spam Filtering Using Statistical Data Compression Models
The Journal of Machine Learning Research
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Artificial Intelligence Review
ECUE: A Spam Filter that Uses Machine Learning to Track Concept Drift
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
IEEE Transactions on Information Theory
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Reexamination of CBR hypothesis
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Effective scheduling strategies for boosting performance on rule-based spam filtering frameworks
Journal of Systems and Software
Automatic case acquisition from texts for process-oriented case-based reasoning
Information Systems
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In this paper, we compare case-based spam filters, focusing on their resilience to concept drift. In particular, we evaluate how to track concept drift using a case-based spam filter that uses a feature-free distance measure based on text compression. In our experiments, we compare two ways to normalise such a distance measure, finding that the one proposed in [1] performs better. We show that a policy as simple as retaining misclassified examples has a hugely beneficial effect on handling concept drift in spam but, on its own, it results in the case base growing by over 30%. We then compare two different retention policies and two different forgetting policies (one a form of instance selection, the other a form of instance weighting) and find that they perform roughly as well as each other while keeping the case base size constant. Finally, we compare a feature-based textual case-based spam filter with our feature-free approach. In the face of concept drift, the feature-based approach requires the case base to be rebuilt periodically so that we can select a new feature set that better predicts the target concept. We find feature-free approaches to have lower error rates than their feature-based equivalents.