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Active learning in multiple-class classification problems via individualized binary models

Author

Listed:
  • Li, Jingjing
  • Chen, Zimu
  • Wang, Zhanfeng
  • Chang, Yuan-chin Ivan

Abstract

We propose a unified algorithm for both categorical and ordinal labeled data in multiclass classification problems, where each subject belongs to one class only. In training an effective classification rule, it is critical that one have and rely on a sufficient amount of reliably labeled data. As information on the training sample sizes needed to obtain satisfactory performance is lacking, we adopt an adaptive subject recruiting scheme with an experimental design criterion to shorten the training process. Because this kind of active learning method is naturally conducted in a sequential manner, we adopt sequential analysis to control the required sample size and ensure the performance of the final classifier. Additionally, we report its statistical properties and numerical results from using synthesized and real data.

Suggested Citation

  • Li, Jingjing & Chen, Zimu & Wang, Zhanfeng & Chang, Yuan-chin Ivan, 2020. "Active learning in multiple-class classification problems via individualized binary models," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    • Handle: RePEc:eee:csdana:v:145:y:2020:i:c:s0167947320300025
    DOI: 10.1016/j.csda.2020.106911
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    References listed on IDEAS

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    1. Deng, Xinwei & Joseph, V. Roshan & Sudjianto, Agus & Wu, C. F. Jeff, 2009. "Active Learning Through Sequential Design, With Applications to Detection of Money Laundering," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 969-981.
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