Scalable and efficient multi-label classification for evolving data streams

@article{Read2012ScalableAE,
  title={Scalable and efficient multi-label classification for evolving data streams},
  author={J. Read and A. Bifet and Geoff Holmes and B. Pfahringer},
  journal={Machine Learning},
  year={2012},
  volume={88},
  pages={243-272}
}
Many challenging real world problems involve multi-label data streams. Efficient methods exist for multi-label classification in non-streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as classifiers must be able to deal with huge numbers of examples and to adapt to change using limited time and memory while being ready to predict at any point.This paper proposes a new experimental framework for learning and evaluating on multi-label data streams, and… Expand
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References

SHOWING 1-10 OF 52 REFERENCES
Efficient multi-label classification for evolving data streams
  • 22
Dealing with Concept Drift and Class Imbalance in Multi-Label Stream Classification
  • 89
  • Highly Influential
  • PDF
An ensemble-based approach to fast classification of multi-label data streams
  • Xiangnan Kong, Philip S. Yu
  • Computer Science
  • 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom)
  • 2011
  • 33
  • Highly Influential
  • PDF
Generating Synthetic Multi-label Data Streams
  • 11
  • PDF
Model-shared subspace boosting for multi-label classification
  • 109
  • PDF
New ensemble methods for evolving data streams
  • 512
  • PDF
Adaptive Learning from Evolving Data Streams
  • 265
Incremental multi-target model trees for data streams
  • 24
  • PDF
ML-KNN: A lazy learning approach to multi-label learning
  • 2,033
  • PDF
Classifier chains for multi-label classification
  • 1,617
  • PDF
...
1
2
3
4
5
...