Batch-mode active learning via error bound minimization

Quanquan Gu, Tong Zhang, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Active learning has been proven to be quite effective in reducing the human labeling efforts by actively selecting the most informative examples to label. In this paper, we present a batch-mode active learning method based on logistic regression. Our key motivation is an out-of-sample bound on the estimation error of class distribution in logistic regression conditioned on any fixed training sample. It is different from a typical PACstyle 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 immediately used to design an active learning algorithm by minimizing this bound iteratively. We also discuss the connections between the proposed method and some existing active learning approaches. Experiments on benchmark UCI datasets and text datasets demonstrate that the proposed method outperforms the state-of-the-art active learning methods significantly.

Original languageEnglish (US)
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014
EditorsNevin L. Zhang, Jin Tian
PublisherAUAI Press
Pages300-309
Number of pages10
ISBN (Electronic)9780974903910
StatePublished - Jan 1 2014
Event30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 - Quebec City, Canada
Duration: Jul 23 2014Jul 27 2014

Publication series

NameUncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014

Other

Other30th Conference on Uncertainty in Artificial Intelligence, UAI 2014
CountryCanada
CityQuebec City
Period7/23/147/27/14

ASJC Scopus subject areas

  • Artificial Intelligence

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