提出分类器链网络，提升多标签分类的预测准确性。
Classifier Chain Networks for Multi-Label Classification
- 构建神经网络结构的分类器链，联合优化参数。
- 在偏离假设条件下仍表现良好，优于多个基准方法。
- 新提出的依赖检测指标可识别标签间条件关系。
分类器链是分析多标签数据集的常用方法。本文提出分类器链网络的泛化形式：分类器链网络，能够联合估计模型参数，并考虑链中前序标签预测对后续分类器的影响。通过模拟实验，评估其性能并与多种基准方法对比，结果表明即使在模型假设不成立的情况下，仍表现出竞争力。此外，提出一种新的标签间条件依赖检测方法，并利用真实数据集验证了分类器链网络的有效性。
原文摘要 · Abstract (English)
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.
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