Ensemble Learning from Crowds
Ensemble Learning from Crowds
复制标题
从人群中进行集成学习
DOI：
10.1109/tkde.2018.2860992
复制
发表时间：
2019
影响因子：
8.9
通讯作者：
Victor S. Sheng
中科院分区：
文献类型：
--
作者：
Jing Zhang（张静）;Min Wu;Victor S. Sheng
传统的众包标签数据学习包括两个阶段：从多个噪声标签中推断出实例的真实标签，并使用推断出的标签构建学习模型。这种简单的两阶段学习方案有两个缺点：(1)推理的准确性可能很低；(2)在推理过程中可能丢失有用的信息。在本文中，我们提出了一种新的群体学习集成方法。我们提出的方法是一个元学习方案。它首先使用引导过程从原始众包标记数据集创建$M$M子数据集。对于每个子数据集，根据其多个噪声标签的分布和类隶属度，以不同的权重复制每个实例。然后从这个扩展的子数据集训练一个基本分类器。最后，通过聚合这些$M$M基分类器的输出来预测未标记的实例。由于该方法消除了推理过程，使用完整的数据集来训练学习模型，尽可能地保留了对学习有用的信息。在9个模拟和2个现实世界众包数据集上的实验结果一致表明，所提出的集成学习方法显著优于5种最先进的方法。
Traditional learning from crowdsourced labeled data consists of two stages: inferring true labels for instances from their multiple noisy labels and building a learning model using these instances with the inferred labels. This straightforward two-stage learning scheme suffers from two weaknesses: (1) the accuracy of inference may be very low; (2) useful information may be lost during inference. In this paper, we proposed a novel ensemble method for learning from crowds. Our proposed method is a meta-learning scheme. It first uses a bootstrapping process to create $M$M sub-datasets from an original crowdsourced labeled dataset. For each sub-dataset, each instance is duplicated with different weights according to the distribution and class memberships of its multiple noisy labels. A base classifier is then trained from this extended sub-dataset. Finally, unlabeled instances are predicted by aggregating the outputs of these $M$M base classifiers. Because the proposed method gets rid of the inference procedure and uses the full dataset to train learning models, it preserves the useful information for learning as much as possible. Experimental results on nine simulated and two real-world crowdsourcing datasets consistently show that the proposed ensemble learning method significantly outperforms five state-of-the-art methods.