In this paper, we address the challenges of random label ordering and limited interpretability associated with Ensemble Classifier Chains (ECC) by introducing a novel ECC method, ECC-MOO&BN, which integrates Bayesian Networks (BN) and Multi-Objective Optimization (MOO). This approach is designed to concurrently overcome these ECC limitations. The ECC-MOO&BN method focuses on extracting diverse and interpretable label orderings for the ECC classifier. We initiated this process by employing mutual information to investigate label relationships and establish the initial structures of the BN. Subsequently, an enhanced NSGA-II algorithm was applied to develop a series of Directed Acyclic Graphs (DAGs) that effectively balance the likelihood and complexity of the BN structure. The rationale behind using the MOO method lies in its ability to optimize both complexity and likelihood simultaneously, which not only diversifies DAG generation but also helps avoid overfitting during the production of label orderings. The DAGs, once sorted topologically, yielded a series of label orderings, which were then seamlessly integrated into the ECC framework for addressing multi-label classification (MLC) problems. Experimental results show that when benchmarked against eleven leading-edge MLC algorithms, our proposed method achieves the highest average ranking across seven evaluation criteria on nine out of thirteen MLC datasets. The results of the Friedman test and Nemenyi test also indicate that the performance of the proposed method has a significant advantage compared to other algorithms.

在本文中，我们通过引入一种新颖的 ECC 方法 ECC-MOO&BN，解决了与集成分类器链 (ECC) 相关的随机标签排序和有限可解释性的挑战，该方法集成了贝叶斯网络 (BN) 和多目标优化 (MOO)。此方法旨在同时克服这些 ECC 限制。 ECC-MOO&BN 方法专注于为 ECC 分类器提取多样化且可解释的标签顺序。我们通过利用互信息来研究标签关系并建立 BN 的初始结构来启动此过程。随后，应用增强的 NSGA-II 算法开发了一系列有向无环图（DAG），有效平衡了 BN 结构的可能性和复杂性。使用 MOO 方法的基本原理在于它能够同时优化复杂性和可能性，这不仅使 DAG 生成多样化，而且有助于避免标签排序过程中的过度拟合。 DAG 经过拓扑排序后，会产生一系列标签排序，然后将其无缝集成到 ECC 框架中，以解决多标签分类 (MLC) 问题。实验结果表明，当针对 11 种前沿 MLC 算法进行基准测试时，我们提出的方法在 13 个 MLC 数据集中的 9 个数据集上的 7 个评估标准中实现了最高平均排名。 Friedman测试和Nemenyi测试的结果也表明该方法的性能相比其他算法具有显着的优势。