Cost-Driven Active Learning with Semi-Supervised Cluster Tree for Text Classification

  • Zhaocai Sun
  • Yunming Ye
  • Yan Li
  • Shengchun Deng
  • Xiaolin Du
Part of the Studies in Computational Intelligence book series (SCI, volume 551)

Abstract

The key idea of active learning is that it can perform better with less data or costs if a machine learner is allowed to choose the data actively. However, the relation between labeling cost and model performance is seldom studied in the literature. In this paper, we thoroughly study this problem and give a criterion called as cost-performance to balance this relation. Based on the criterion, a cost-driven active SSC algorithm is proposed, which can stop the active process automatically. Empirical results show that our method outperforms active SVM and co-EMT.

Keywords

Active Learning Semi-supervised Learning Cluster Tree 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zhaocai Sun
    • 2
  • Yunming Ye
    • 2
  • Yan Li
    • 1
  • Shengchun Deng
    • 3
  • Xiaolin Du
    • 2
  1. 1.Shenzhen Graduate SchoolHarbin Institute of TechnologyWeihaiChina
  2. 2.School of Computer EngineeringShenzhen PolytechnicShenzhenChina
  3. 3.Department of Computer ScienceHarbin Institute of TechnologyWeihaiChina

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