C4.5: programs for machine learning
C4.5: programs for machine learning
Efficient incremental induction of decision trees
Machine Learning
Representation of propositional expert systems as partial functions
Artificial Intelligence
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
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient decision tree construction on streaming data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Ambiguous decision trees for mining concept-drifting data streams
Pattern Recognition Letters
Adaptive classifier selection based on two level hypothesis tests for incremental learning
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning approach or ensemble classifiers approach. However, both of them can not make a prediction at any time exactly. In this paper, we propose a novel strategy for the maintenance of knowledge. Our approach stores and maintains knowledge in ambiguous decision table with current statistical indicators. With our disambiguation algorithm, a decision tree without any time problem can be synthesized on the fly efficiently. Our experiment results have shown that the accuracy rate of our approach is higher and smoother than other approaches. So, our algorithm is demonstrated to be a real anytime approach.