Instance-Based Learning Algorithms
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
Machine Learning - Special issue on context sensitivity and concept drift
Adaptive information filtering: detecting changes in text streams
Proceedings of the eighth international conference on Information and knowledge management
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Learning from Noisy Data
Machine Learning
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Diagnosis and Decision Support
Case-Based Reasoning Technology, From Foundations to Applications
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Using latent semantic indexing to filter spam
Proceedings of the 2003 ACM symposium on Applied computing
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Dynamic integration of classifiers for handling concept drift
Information Fusion
Artificial Intelligence Review
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
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
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
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
Combining neural networks and semantic feature space for email classification
Knowledge-Based Systems
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
Learning, detecting, understanding, and predicting concept changes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Dynamic financial distress prediction using instance selection for the disposal of concept drift
Expert Systems with Applications: An International Journal
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
Applied Soft Computing
EGAL: exploration guided active learning for TCBR
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Detecting change via competence model
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Modified blame-based noise reduction for concept drift
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
SDAI: An integral evaluation methodology for content-based spam filtering models
Expert Systems with Applications: An International Journal
Drift mining in data: A framework for addressing drift in classification
Computational Statistics & Data Analysis
Grindstone4Spam: An optimization toolkit for boosting e-mail classification
Journal of Systems and Software
Tracking concept drift in malware families
Proceedings of the 5th ACM workshop on Security and artificial intelligence
Developing methods and heuristics with low time complexities for filtering spam messages
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
Sublinear algorithms for penalized logistic regression in massive datasets
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
An efficient adversarial learning strategy for constructing robust classification boundaries
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Spam e-mail classification based on the IFWB algorithm
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
RCD: A recurring concept drift framework
Pattern Recognition Letters
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Proceedings of the 10th Working Conference on Mining Software Repositories
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Concept drift detection via competence models
Artificial Intelligence
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Spam filtering is a particularly challenging machine learning task as the data distribution and concept being learned changes over time. It exhibits a particularly awkward form of concept drift as the change is driven by spammers wishing to circumvent spam filters. In this paper we show that lazy learning techniques are appropriate for such dynamically changing contexts. We present a case-based system for spam filtering that can learn dynamically. We evaluate its performance as the case-base is updated with new cases. We also explore the benefit of periodically redoing the feature selection process to bring new features into play. Our evaluation shows that these two levels of model update are effective in tracking concept drift.