A probabilistic model of active learning with multiple noisy oracles
A probabilistic model of active learning with multiple noisy oracles
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DOI：
10.1016/j.neucom.2013.02.034
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发表时间：
2013-10
期刊：
影响因子：
6
通讯作者：
Weining Wu;Yang Liu;Maozu Guo;Chun-yu Wang;Xiaoyan Liu
中科院分区：
文献类型：
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作者：
Weining Wu;Yang Liu;Maozu Guo;Chun-yu Wang;Xiaoyan Liu
在本文中，我们专注于在主动学习中获得准确的分类器，当有多个具有不同和未知水平的专业知识的嘈杂预言机为选定的实例提供标签时。我们提出了一个概率模型的主动学习与多个嘈杂的预言机（PMActive）。我们的目标是公式化的选择最可靠的预言和估计训练数据的实际标签。当在每一轮主动学习中选择一个实例时，我们首先根据观察到的噪声标签对单个预言机的准确性进行建模，并选择最可靠的预言机为实例提供标签。在将新的实例-标签对添加到训练集中之后，实例的实际标签被估计并用于增强当前分类器的性能。实验结果表明，PMActive方法可以工作在不同的甲骨文噪声水平。与主动学习中常用的基线相比，PMActive方法在获得更准确的分类器方面具有上级优势。
In this paper, we focus on obtaining an accurate classifier in active learning, when there are multiple noisy oracles with different and unknown levels of expertise to provide labels for selected instances. We propose a probabilistic model of active learning with multiple noisy oracles (PMActive). Our goal is formulized as to select the most reliable oracle and estimate the actual label on training data. When an instance is selected in every round of active learning, we firstly model the accuracies of individual oracles based on observed noisy labels, and select the most reliable oracle of all to provide a label for the instance. After adding the new instance-label pair into the training set, the actual label of the instance is estimated and used for enhancing the performance of the current classifier. The experimental results indicate that the PMActive method can work with different noise levels of oracles. Compared with the baselines which are commonly used in this area of active learning, the PMActive method is superior in obtaining a more accurate classifier.