Computer networks
FAVORIT: concept formation with ageing of knowledge
Pattern Recognition Letters
C4.5: programs for machine learning
C4.5: programs for machine learning
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
Machine Learning - Special issue on evaluating and changing representation
Learning in the presence of concept drift and hidden contexts
Machine Learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Selecting Examples for Partial Memory Learning
Machine Learning
Machine Learning
Incremental Learning from Noisy Data
Machine Learning
Machine Learning by Function Decomposition
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Incremental Learning with Partial Instance Memory
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners
The Journal of Machine Learning Research
Extreme value dependence in problems with a changing causation structure
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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Learning systems need to face several problems: incrementality, tracking concept drift, robustness to noise and recurring contexts in order to operate continuously. A method for on-line induction of decision trees motivated by the above requirements is presented. It uses the following strategy: creating a delayed window in every node for applying forgetting mechanisms; automatic modification of the delayed window; and constructive induction for identifying recurring contexts. The default configuration of the proposed approach has shown to be globally efficient, reactive, robust and problem-independent, which is suitable for problems with unknown dynamics. Notable results have been obtained when noise and concept drift are present.