The weighted majority algorithm
Information and Computation
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
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
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
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Issues in data stream management
ACM SIGMOD Record
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient decision tree construction on streaming data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2004 ACM symposium on Applied computing
Discovering decision rules from numerical data streams
Proceedings of the 2004 ACM symposium on Applied computing
Incremental learning with partial instance memory
Artificial Intelligence
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
A framework for generating data to simulate changing environments
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Efficient instance-based learning on data streams
Intelligent Data Analysis
Mining frequent itemsets in data streams using the weighted sliding window model
Expert Systems with Applications: An International Journal
Subrule analysis and the frequency-confidence diagram
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
A bounded version of online boosting on open-ended data streams
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Generating recommendations for consensus negotiation in group personalization services
Personal and Ubiquitous Computing
Pattern Recognition Letters
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Mining data streams is a challenging task that requires online systems based on incremental learning approaches. This paper describes a classification system based on decision rules that may store up-to-date border examples to avoid unnecessary revisions when virtual drifts are present in data. Consistent rules classify new test examples by covering and inconsistent rules classify them by distance as the nearest neighbor algorithm. In addition, the system provides an implicit forgetting heuristic so that positive and negative examples are removed from a rule when they are not near one another.