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
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Online ensemble learning
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Tracking Recurring Concepts with Meta-learners
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Knowledge and Information Systems
The Journal of Machine Learning Research
Tracking recurrent concepts using context
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Incremental Learning of Concept Drift in Nonstationary Environments
IEEE Transactions on Neural Networks
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Handling recurring concepts has become of interest as a challenging problem in the field of data stream classification in recent years. One main feature of data streams is that they appear in nonstationary environments. This means that the concept which the data are drawn from, changes over the time. If after a long enough time, the concept reverts to one of the previous concepts, it is said that recurring concepts has occurred. One solution to this challenge is to maintain a pool of classifiers, each representing a concept in the stream. This paper follows this approach and holds an ensemble of classifiers for each concept. As for each received batch of data, a new classifier is created; there will be a huge amount of classifiers which could not be maintained in the pool. To handle the memory limitations, a maximum number of concepts and classifiers are assumed. So the necessity of managing the concepts and classifiers is obvious. This paper presents a novel algorithm to manage the pool. Some pool management operations including merging and splitting the concepts are introduced. Experimental results show the performance dominance of using our method to the most promising stream classification algorithms.