Self-Taught Active Learning from Crowds
Self-Taught Active Learning from Crowds
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DOI：
10.1109/icdm.2012.64
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Meng Fang;Xingquan Zhu;Bin Li;W. Ding;Xindong Wu
中科院分区：
文献类型：
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作者：
Meng Fang;Xingquan Zhu;Bin Li;W. Ding;Xindong Wu
社交标签和众包系统的出现提供了一个独特的平台，多个弱标签者可以形成一个群体来完成标签任务。然而，群体贴标者通常吵闹、不准确，并且贴标知识有限，最糟糕的是，他们独立行动，没有寻求彼此互补的知识来提高贴标性能。在本文中，我们提出了一种自学主动学习（STAL）范式，其中不完美的标记者能够相互学习互补的知识，以扩展他们的知识集并使底层的主动学习者受益。我们采用概率模型来表征每个标记者的知识，通过该模型，弱标记者可以从更强的标记者那里学习补充知识。因此，自学的主动学习过程最终有助于以最小化的标记成本和标记错误实现高分类精度。
The emergence of social tagging and crowdsourcing systems provides a unique platform where multiple weak labelers can form a crowd to fulfill a labeling task. Yet crowd labelers are often noisy, inaccurate, and have limited labeling knowledge, and worst of all, they act independently without seeking complementary knowledge from each other to improve labeling performance. In this paper, we propose a Self-Taught Active Learning (STAL) paradigm, where imperfect labelers are able to learn complementary knowledge from one another to expand their knowledge sets and benefit the underlying active learner. We employ a probabilistic model to characterize the knowledge of each labeler through which a weak labeler can learn complementary knowledge from a stronger peer. As a result, the self-taught active learning process eventually helps achieve high classification accuracy with minimized labeling costs and labeling errors.