The Journal of Machine Learning Research
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners
The Journal of Machine Learning Research
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Cascade Multiple Classifier System for Document Categorization
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Adaptive ensemble based learning in non-stationary environments with variable concept drift
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Approaches to adversarial drift
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
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Any change in the classification problem in the course of online classification is termed changing environments. Examples of changing environments include change in the underlying data distribution, change in the class definition, adding or removing a feature. The two general strategies for handling changing environments are (i) constant update of the classifier and (ii) re-training of the classifier after change detection. The former strategy is useful with gradual changes while the latter is useful with abrupt changes. If the type of changes is not known in advance, a combination of the two strategies may be advantageous. We propose a classifier ensemble using Winnow. For the constant-update strategy we used the nearest neighbour with a fixed size window and two methods with a learning rate: the online perceptron and an online version of the linear discriminant classifier (LDC). For the detect-and-retrain strategy we used the nearest neighbour classifier and the online LDC. Experiments were carried out on 28 data sets and 3 different scenarios: no change, gradual change and abrupt change. The results indicate that the combination works better than each strategy on its own.