Tracking Recurring Concepts with Meta-learners
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Tracking recurrent concepts using context
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
Applied Soft Computing
Learning recurring concepts from data streams with a context-aware ensemble
Proceedings of the 2011 ACM Symposium on Applied Computing
Artificial recurrence for classification of streaming data with concept shift
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Learning about the learning process
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Mining Recurring Concept Drifts with Limited Labeled Streaming Data
ACM Transactions on Intelligent Systems and Technology (TIST)
RCD: A recurring concept drift framework
Pattern Recognition Letters
Tracking recurrent concepts using context
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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Streaming data may consist of multiple drifting concepts each having its own underlying data distribution. We present an ensemble learning based approach to handle the data streams having multiple underlying modes. We build a global set of classifiers from sequential data chunks; ensembles are then selected from this global set of classifiers, and new classifiers created if needed, to represent the current concept in the stream. The system is capable of performing any-time classification and to detect concept drift in the stream. In streaming data historic concepts are likely to reappear so we dont delete any of the historic classifiers. Instead, we judiciously select only pertinent classifiers from the global set while forming the ensemble set for a classification task.