HyDI：一种用于多标签分类的深度学习-归纳逻辑编程混合集成方法
HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classification
- University of Osnabrück（奥斯纳布吕克大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
HyDI提出一种结合深度学习与归纳逻辑编程的混合集成架构，用于层次化多标签分类，在ChEBI本体上为314个类别生成规则，提供透明且可解释的分类结果。
AI中文摘要：
尽管深度学习模型在许多应用中取得了显著成果，但其难以解释是众所周知的。本文提出了HyDI，一种用于层次化多标签分类的混合集成架构。它将深度学习（DL）模型与归纳逻辑编程（ILP）生成的基于规则的分类器相结合。对于标签层次结构中的叶类，基于规则的分类器替代了深度学习模型，从而产生更透明的分类结果。HyDI被应用于生物兴趣化学实体（ChEBI）本体，为314个类别提供了ILP生成的规则。对于这些类别，HyDI可以生成全局解释以及结合视觉和文本描述的局部解释。
英文摘要：
While attaining remarkable results for many applications, Deep Learning models are notoriously difficult to explain. This work introduces HyDI, a hybrid ensemble architecture for hierarchical multi-label classification. It combines a Deep Learning (DL) model with rule-based classifiers generated by Inductive Logic Programming (ILP). For leaf classes of the label hierarchy, the rule-based classifiers replace the DL model, leading to more transparent classification results. HyDI is applied to the Chemical Entities of Biological Interest (ChEBI) ontology, providing ILP-generated rules for 314 classes. For these classes, HyDI can generate global explanations as well as local explanations that combine visual and text-based descriptions.
