Batch-Mode Active Learning via Error Bound Minimization

Quanquan Gu CS UIUC, Tong Zhang Rutgers University, Jiawei Han CS UIUC
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:411-420, 2014.

Abstract

Active learning has been proven to be quite effec- tive in reducing the human labeling efforts by ac- tively selecting the most informative examples to label. In this paper, we present a batch-mode ac- tive learning method based on logistic regression. Our key motivation is an out-of-sample bound on the estimation error of class distribution in lo- gistic regression conditioned on any fixed train- ing sample. It is different from a typical PAC- style passive learning error bound, that relies on the i.i.d. assumption of example-label pairs. In addition, it does not contain the class labels of the training sample. Therefore, it can be imme- diately used to design an active learning algo- rithm by minimizing this bound iteratively. We also discuss the connections between the pro- posed method and some existing active learn- ing approaches. Experiments on benchmark UCI datasets and text datasets demonstrate that the proposed method outperforms the state-of-the-art active learning methods significantly.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-uiuc14a, title = {Batch-Mode Active Learning via Error Bound Minimization}, author = {UIUC, Quanquan Gu CS and University, Tong Zhang Rutgers and UIUC, Jiawei Han CS}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {411--420}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/uiuc14a/uiuc14a.pdf}, url = {https://proceedings.mlr.press/r12/uiuc14a.html}, abstract = {Active learning has been proven to be quite effec- tive in reducing the human labeling efforts by ac- tively selecting the most informative examples to label. In this paper, we present a batch-mode ac- tive learning method based on logistic regression. Our key motivation is an out-of-sample bound on the estimation error of class distribution in lo- gistic regression conditioned on any fixed train- ing sample. It is different from a typical PAC- style passive learning error bound, that relies on the i.i.d. assumption of example-label pairs. In addition, it does not contain the class labels of the training sample. Therefore, it can be imme- diately used to design an active learning algo- rithm by minimizing this bound iteratively. We also discuss the connections between the pro- posed method and some existing active learn- ing approaches. Experiments on benchmark UCI datasets and text datasets demonstrate that the proposed method outperforms the state-of-the-art active learning methods significantly.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Batch-Mode Active Learning via Error Bound Minimization %A Quanquan Gu CS UIUC %A Tong Zhang Rutgers University %A Jiawei Han CS UIUC %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-uiuc14a %I PMLR %P 411--420 %U https://proceedings.mlr.press/r12/uiuc14a.html %V R12 %X Active learning has been proven to be quite effec- tive in reducing the human labeling efforts by ac- tively selecting the most informative examples to label. In this paper, we present a batch-mode ac- tive learning method based on logistic regression. Our key motivation is an out-of-sample bound on the estimation error of class distribution in lo- gistic regression conditioned on any fixed train- ing sample. It is different from a typical PAC- style passive learning error bound, that relies on the i.i.d. assumption of example-label pairs. In addition, it does not contain the class labels of the training sample. Therefore, it can be imme- diately used to design an active learning algo- rithm by minimizing this bound iteratively. We also discuss the connections between the pro- posed method and some existing active learn- ing approaches. Experiments on benchmark UCI datasets and text datasets demonstrate that the proposed method outperforms the state-of-the-art active learning methods significantly. %Z Reissued by PMLR on 04 October 2026.
APA
UIUC, Q.G.C., University, T.Z.R. & UIUC, J.H.C.. (2014). Batch-Mode Active Learning via Error Bound Minimization. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:411-420 Available from https://proceedings.mlr.press/r12/uiuc14a.html. Reissued by PMLR on 04 October 2026.

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