The labeling process within a supervised learning task is usually carried out by an expert, which provides the ground truth (gold standard) for each sample. However, in many real-world applications, we typically have access to annotations provided by crowds holding different and unknown expertise levels. Learning from crowds (LFC) intends to configure machine learning paradigms in the presence of multilabelers, residing on two key assumptions: the labeler’s performance does not depend on the input space, and independence among the annotators is imposed. Here, we propose the correlated chained Gaussian processes from the multiple annotators (CCGPMA) approach, which models each annotator’s performance as a function of the input space and exploits the correlations among experts. Experimental results associated with classification and regression tasks show that our CCGPMA performs better modeling of the labelers’ behavior, indicating that it consistently outperforms other state-of-the-art LFC approaches.

中文翻译：

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具有多个注释器的数据集的相关链式高斯过程

监督学习任务中的标记过程通常由专家执行，为每个样本提供基本事实（黄金标准）。然而，在许多现实世界的应用程序中，我们通常可以访问由拥有不同且未知专业水平的人群提供的注释。从人群中学习（LFC）旨在在多标签器存在的情况下配置机器学习范例，基于两个关键假设：标签器的性能不依赖于输入空间，并且强加注释器之间的独立性。在这里，我们提出了来自多个注释器（CCGPMA）方法的相关链式高斯过程，该方法将每个注释器的性能建模为输入空间的函数，并利用专家之间的相关性。与分类和回归任务相关的实验结果表明，我们的 CCGPMA 对贴标者的行为进行了更好的建模，这表明它始终优于其他最先进的 LFC 方法。