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Data Mining and Knowledge Discovery

, Volume 31, Issue 1, pp 164–202 | Cite as

Evidence-based uncertainty sampling for active learning

  • Manali Sharma
  • Mustafa BilgicEmail author
Article

Abstract

Active learning methods select informative instances to effectively learn a suitable classifier. Uncertainty sampling, a frequently utilized active learning strategy, selects instances about which the model is uncertain but it does not consider the reasons for why the model is uncertain. In this article, we present an evidence-based framework that can uncover the reasons for why a model is uncertain on a given instance. Using the evidence-based framework, we discuss two reasons for uncertainty of a model: a model can be uncertain about an instance because it has strong, but conflicting evidence for both classes or it can be uncertain because it does not have enough evidence for either class. Our empirical evaluations on several real-world datasets show that distinguishing between these two types of uncertainties has a drastic impact on the learning efficiency. We further provide empirical and analytical justifications as to why distinguishing between the two uncertainties matters.

Keywords

Active learning Uncertainty sampling Classification 

Notes

Acknowledgments

This material is based upon work supported by the National Science Foundation CAREER award no. IIS-1350337.

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Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Illinois Institute of TechnologyChicagoUSA

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