Active Learning With Drifting Streaming Data

@article{liobait2014ActiveLW,
  title={Active Learning With Drifting Streaming Data},
  author={I. Žliobaitė and A. Bifet and B. Pfahringer and Geoff Holmes},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2014},
  volume={25},
  pages={27-39}
}
In learning to classify streaming data, obtaining true labels may require major effort and may incur excessive cost. Active learning focuses on carefully selecting as few labeled instances as possible for learning an accurate predictive model. Streaming data poses additional challenges for active learning, since the data distribution may change over time (concept drift) and models need to adapt. Conventional active learning strategies concentrate on querying the most uncertain instances, which… Expand
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246 Citations
Highly Influential Citations
38
Background Citations
129
Methods Citations
68
Results Citations
6

246 Citations

Citation Type

Citation Type

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