Rule-Based Multi-label Classification: Challenges and Opportunities
Rule-Based Multi-label Classification: Challenges and Opportunities
复制标题
DOI：
10.1007/978-3-030-57977-7_1
复制
发表时间：
2020-06
期刊：
影响因子：
--
通讯作者：
Eyke Hüllermeier;Johannes Fürnkranz;E. Mencía;Vu-Linh Nguyen;Michael Rapp
中科院分区：
文献类型：
--
作者：
Eyke Hüllermeier;Johannes Fürnkranz;E. Mencía;Vu-Linh Nguyen;Michael Rapp
在多标签分类（MLC）的上下文中，基于规则的学习算法具有许多吸引人的特性，这些特性是其他方法所没有的，至少整体上没有。这包括规则的潜在可解释性，它们以灵活的方式建模（局部）标记依赖关系的能力，以及对不同损失函数的预测器的方便定制。在本文中，我们提出了一个基于规则的MLC模块化框架，并讨论了多标签规则学习的相关挑战和机遇。
In the context of multi-label classification (MLC), rule-based learning algorithms have a number of appealing properties that are not, at least not as a whole, shared by other approaches. This includes the potential interpretability of rules, their ability to model (local) label dependencies in a flexible way, and the facile customization of a predictor to different loss functions. In this paper, we present a modular framework for rule-based MLC and discuss related challenges and opportunities for multi-label rule learning.