The classifier chain is a widely used method for analyzing multi-labeled data sets. In this study, we introduce a generalization of the classifier chain: the classifier chain network. The classifier chain network enables joint estimation of model parameters, and allows to account for the influence of earlier label predictions on subsequent classifiers in the chain. Through simulations, we evaluate the classifier chain network's performance against multiple benchmark methods, demonstrating competitive results even in scenarios that deviate from its modeling assumptions. Furthermore, we propose a new measure for detecting conditional dependencies between labels and illustrate the classifier chain network's effectiveness using an empirical data set.

翻译：分类器链是分析多标签数据集的常用方法。本研究提出了一种广义化的分类器链：分类器链网络。该网络能够实现模型参数的联合估计，并能够考虑链中早期标签预测对后续分类器的影响。通过仿真实验，我们将分类器链网络与多种基准方法进行性能比较，结果表明即使在与建模假设存在偏差的场景下，该网络仍能取得具有竞争力的结果。此外，我们提出了一种检测标签间条件依赖关系的新度量方法，并通过实证数据集展示了分类器链网络的有效性。