Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Tracking drifting concepts using random examples
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Learning time-varying concepts
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Experience with a learning personal assistant
Communications of the ACM
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
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Incremental relevance feedback for information filtering
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A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An adaptive Web page recommendation service
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Data mining: practical machine learning tools and techniques with Java implementations
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Mining high-speed data streams
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Mining time-changing data streams
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A streaming ensemble algorithm (SEA) for large-scale classification
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A Multistrategy Approach to the Classification of Phases in Business Cycles
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Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Estimating the Generalization Performance of an SVM Efficiently
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Mining concept-drifting data streams using ensemble classifiers
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Editorial message: special track on data streams
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Lossless Online Bayesian Bagging
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Systematic data selection to mine concept-drifting data streams
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Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
Editorial message: special track on data streams
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Learning drifting concepts: Example selection vs. example weighting
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WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
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Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
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Incremental learning in nonstationary environments with controlled forgetting
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Unsupervised change analysis using supervised learning
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Ubiquitous knowledge discovery
On-line learning: where are we so far?
Ubiquitous knowledge discovery
Random ensemble decision trees for learning concept-drifting data streams
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Online non-stationary boosting
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Dynamic multi-objective evolution of classifier ensembles for video face recognition
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A survey on concept drift adaptation
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Tracking recurrent concepts using context
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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In many real-world classification tasks, data arrives over time and the target concept to be learned from the data stream may change over time. Boosting methods are well-suited for learning from data streams, but do not address this concept drift problem. This paper proposes a boosting-like method to train a classifier ensemble from data streams that naturally adapts to concept drift. Moreover, it allows to quantify the drift in terms of its base learners. Similar as in regular boosting, examples are re-weighted to induce a diverse ensemble of base models. In order to handle drift, the proposed method continuously re-weights the ensemble members based on their performance on the most recent examples only. The proposed strategy adapts quickly to different kinds of concept drift. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It performs no worse than advanced adaptive time window and example selection strategies that store all the data and are thus not suited for mining massive streams. The proposed algorithm has low computational costs.