Learning in the presence of concept drift and hidden contexts
Machine Learning
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Probabilistic Noise Identification and Data Cleaning
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting in the presence of noise
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Online classification of nonstationary data streams
Intelligent Data Analysis
Robust ensemble learning for mining noisy data streams
Decision Support Systems
An efficient ensemble method for classifying skewed data streams
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
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The two main challenges associated with mining data streams are concept drifting and data noise. Current algorithms mainly depend on the robust of the base classifier or learning ensembles, and have no active mechanisms to deal noisy. However, noise still can induce the drastic drops in accuracy. In this paper, we present a clustering-based method to filter out hard instances and noise instances from data streams. We also propose a trigger to detect concept drifting and build RobustBoosting, an ensemble classifier, by boosting the hard instances. We evaluated RobustBoosting algorithm and AdaptiveBoosting algorithm [1] on the synthetic and real-life data sets. The experiment results show that the proposed method has substantial advantage over AdaptiveBoosting algorithm in prediction accuracy, and that it can converge to target concepts efficiently with high accuracy on datasets with noise level as high as 40%.