Confidence-based active learning
Confidence-based active learning
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DOI：
10.1109/tpami.2006.156
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发表时间：
2006-08-01
影响因子：
23.6
通讯作者：
Sethi, Ishwar K.
中科院分区：
文献类型：
--
作者：
Li, Mingkun;Sethi, Ishwar K.
本文提出了一种新的主动学习方法，基于置信度的主动学习，用于训练广泛的分类器。这种方法是基于识别和注释不确定的样本。每个样本的不确定度值由其条件误差来衡量。该方法利用了当前分类器的概率保持和排序特性。它校准分类器的输出分数的条件错误。因此，它可以根据其来自分类器的输出分数来估计每个输入样本的不确定性值，并且仅选择具有高于用户定义的阈值的不确定性值的样本。即使我们不能保证所提出的方法的最优性，我们发现它提供了良好的性能。与现有的方法相比，该方法是强大的，没有额外的计算工作。在此基础上，提出了一种新的支持向量机主动学习方法。提出了一种动态面元宽度分配方法来精确估计样本条件误差，该方法适应于潜在概率。使用合成和真实的数据集证明了该方法的有效性，并将其性能与广泛使用的最小确定性主动学习方法进行了比较。
This paper proposes a new active learning approach, confidence- based active learning, for training a wide range of classifiers. This approach is based on identifying and annotating uncertain samples. The uncertainty value of each sample is measured by its conditional error. The approach takes advantage of current classifiers' probability preserving and ordering properties. It calibrates the output scores of classifiers to conditional error. Thus, it can estimate the uncertainty value for each input sample according to its output score from a classifier and select only samples with uncertainty value above a user- defined threshold. Even though we cannot guarantee the optimality of the proposed approach, we find it to provide good performance. Compared with existing methods, this approach is robust without additional computational effort. A new active learning method for support vector machines ( SVMs) is implemented following this approach. A dynamic bin width allocation method is proposed to accurately estimate sample conditional error and this method adapts to the underlying probabilities. The effectiveness of the proposed approach is demonstrated using synthetic and real data sets and its performance is compared with the widely used least certain active learning method.