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
Machine Learning - Special issue on context sensitivity and concept drift
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
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An ensemble approach for incremental learning in nonstationary environments
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Detecting concept drift using statistical testing
DS'07 Proceedings of the 10th international conference on Discovery science
Adaptive methods for classification in arbitrarily imbalanced and drifting data streams
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Mining Recurring Concept Drifts with Limited Labeled Streaming Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Combining block-based and online methods in learning ensembles from concept drifting data streams
Information Sciences: an International Journal
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Most machine learning algorithms assume stationary environments, require a large number of training examples in advance, and begin the learning from scratch. In contrast, humans learn in changing environments with sequential training examples and leverage prior knowledge in new situations. To deal with real-world problems in changing environments, the ability to make human-like quick responses must be developed in machines. Many researchers have presented learning systems that assume the presence of hidden context and concept drift. In particular, several systems have been proposed that use ensembles of classifiers on sequential chunks of training examples. These systems can respond to gradual changes in large-scale data streams but have problems responding to sudden changes and leveraging prior knowledge of recurring contexts. Moreover, these are not pure online learning systems. We propose an online learning system that uses an ensemble of classifiers suited to recent training examples. We use experiments to show that this system can leverage prior knowledge of recurring contexts and is robust against various noise levels and types of drift.