C4.5: programs for machine learning
C4.5: programs for machine learning
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Diagnosis and Decision Support
Case-Based Reasoning Technology, From Foundations to Applications
Using latent semantic indexing to filter spam
Proceedings of the 2003 ACM symposium on Applied computing
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Time-efficient spam e-mail filtering using n-gram models
Pattern Recognition Letters
Artificial Intelligence Review
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
k-NN Aggregation with a Stacked Email Representation
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Email Spam Filtering: A Systematic Review
Foundations and Trends in Information Retrieval
Using the self organizing map for clustering of text documents
Expert Systems with Applications: An International Journal
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
Boosting CBR Agents with Genetic Algorithms
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Managing computer files via artificial intelligence approaches
Artificial Intelligence Review
Introducing attribute risk for retrieval in case-based reasoning
Knowledge-Based Systems
Intelligent system applications in electronic tourism
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Generating estimates of classification confidence for a case-based spam filter
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Segmental parameterisation and statistical modelling of e-mail headers for spam detection
Information Sciences: an International Journal
User action based adaptive learning with weighted bayesian classification for filtering spam mail
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
CBTV: visualising case bases for similarity measure design and selection
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
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Because of the changing nature of spam, a spam filtering system that uses machine learning will need to be dynamic. This suggests that a case-based (memory-based) approach may work well. Case-Based Reasoning (CBR) is a lazy approach to machine learning where induction is delayed to run time. This means that the case base can be updated continuously and new training data is immediately available to the induction process. In this paper we present a detailed description of such a system called ECUE and evaluate design decisions concerning the case representation. We compare its performance with an alternative system that uses Naïve Bayes. We find that there is little to choose between the two alternatives in cross-validation tests on data sets. However, ECUE does appear to have some advantages in tracking concept drift over time.