Uncertainty-aware complementary label queries for active learning

Shengyuan LIU; Ke CHEN; Tianlei HU; Yunqing MAO

doi:10.1631/FITEE.2200589

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Uncertainty-aware complementary label queries for active learning

Regular Papers | Updated：2023-10-25

- Uncertainty-aware complementary label queries for active learning
- 基于主动学习的不确定性感知补标签查询
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 10, Pages: 1497-1503(2023)
- Affiliations：
  
  1.Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
  2.State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou 310027, China
  3.City Cloud Technology (China) Co., Ltd., Hangzhou 310000, China
- Author bio：
  
  E-mail: liushengyuan@zju.edu.cn
  ‡Corresponding author
  htl@zju.edu.cn
  myq@citycloud.com.cn
- Funds：
- DOI：10.1631/FITEE.2200589
  CLC：
- Received：23 November 2022，
  
  Accepted：20 April 2023，
  
  Published：0 October 2023
- Accepted：
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Shengyuan LIU, Ke CHEN, Tianlei HU, et al. Uncertainty-aware complementary label queries for active learning[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(10): 1497-1503.
DOI：

Shengyuan LIU, Ke CHEN, Tianlei HU, et al. Uncertainty-aware complementary label queries for active learning[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(10): 1497-1503. DOI： 10.1631/FITEE.2200589.

摘要

许多主动学习方法假设学习者可便捷地向注释者询问训练数据的完整标注信息。这些方法主要试图通过最小化标注数量降低标注成本。然而，对于许多现实中的分类任务来说，精确标注实例仍然非常昂贵。为降低单次标注行为成本，本文试图解决一种新的主动学习范式，称为具有补标签的主动学习（ALCL）。ALCL学习器只针对样例特定类别提出是或否的问题。在收到标注者答案后，ALCL学习器获得一些有监督实例和更多具有补标签的训练实例，这些补标签仅表示对应标签与该实例无关。。ALCL具有两个挑战性问题：如何选择要查询的实例以及如何从这些补标签和普通标签中提取信息。针对第一个问题，在主动学习范式下提出一种基于不确定性的抽样策略。针对第二个问题，改进了一种已有的ALCL方法，同时适配了我们的抽样策略。在各种数据集上的实验结果验证了本文方法的有效性。

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references

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