Active Learning with c-Certainty
Active Learning with c-Certainty
复制标题
具有 c-确定性的主动学习
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
C. Ling
中科院分区：
文献类型：
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作者：
Eileen A. Ni;C. Ling
标签中的噪声会降低主动学习的性能。为了减少噪声，已经提出了在多个预言机上的工作。但是，仍然没有办法保证标签质量。此外，大多数以前的作品假设甲骨文的噪音水平是均匀分布的或独立于示例的，这可能是不现实的。在本文中，我们提出了一种新的主动学习范式，其中预言机可以返回标签和置信度。在这种模式下，我们提出了一种新的和有效的主动学习策略，可以保证质量的标签查询多个预言机。此外，我们去除了上述之前工作的假设，并设计了一种能够选择最佳预言机进行查询的新颖算法。我们的实证研究表明，新算法是强大的，它表现出良好的与给定的不同类型的预言机。据我们所知，这是第一个工作，提出了这种新的主动学习范式和主动学习算法，其中的标签质量得到保证。
It is well known that the noise in labels deteriorates the performance of active learning. To reduce the noise, works on multiple oracles have been proposed. However, there is still no way to guarantee the label quality. In addition, most previous works assume that the noise level of oracles is evenly distributed or example-independent which may not be realistic. In this paper, we propose a novel active learning paradigm in which oracles can return both labels and confidences. Under this paradigm, we then propose a new and effective active learning strategy that can guarantee the quality of labels by querying multiple oracles. Furthermore, we remove the assumptions of the previous works mentioned above, and design a novel algorithm that is able to select the best oracles to query. Our empirical study shows that the new algorithm is robust, and it performs well with given different types of oracles. As far as we know, this is the first work that proposes this new active learning paradigm and an active learning algorithm in which label quality is guaranteed.