Online Asymmetric Active Learning with Imbalanced Data

@inproceedings{Zhang2016OnlineAA,
  title={Online Asymmetric Active Learning with Imbalanced Data},
  author={Xiaoxuan Zhang and Tianbao Yang and Padmini Srinivasan},
  booktitle={KDD},
  year={2016}
}
This paper considers online learning with imbalanced streaming data under a query budget, where the act of querying for labels is constrained to a budget limit. We study different active querying strategies for classification. In particular, we propose an asymmetric active querying strategy that assigns different probabilities for query to examples predicted as positive and negative. To corroborate the proposed asymmetric query model, we provide a theoretical analysis on a weighted mistake… CONTINUE READING
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