Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

2-2017

Abstract

This paper investigates the problem of online active learning for training classification models from sequentially arriving data. This is more challenging than conventional online learning tasks since the learner not only needs to figure out how to effectively update the classifier but also needs to decide when is the best time to query the label of an incoming instance given limited label budget. The existing online active learning approaches are often based on first-order online learning methods which generally fall short in slow convergence rate and suboptimal exploitation of available information when querying the labeled data. To overcome the limitations, in this paper, we present a new framework of Second-order Online Active Learning (SOAL), which fully exploits both first-order and second-order information to achieve high learning accuracy with low labeling cost. We conduct both theoretical analysis and empirical studies for evaluating the proposed SOAL algorithm extensively. The encouraging results show clear advantages of the proposed algorithm over a family of state-of-The-Art online active learning algorithms.

Keywords

online learning, active learning, machine learning

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

ICDM 2016: Proceedings of IEEE International Conference on Data Mining: Barcelona, Spain, December 12-15

First Page

931

Last Page

936

ISBN

9781509054749

Identifier

10.1109/ICDM.2016.0115

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICDM.2016.185

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