论文标题
fold-rm：用于混合数据的多类分类的可扩展，高效且可解释的归纳学习算法
FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
论文作者
论文摘要
FOLD-RM是一种用于学习混合（数值和分类）数据的学习默认规则的自动归纳学习算法。它为多类别分类任务生成（可解释的）答案集编程（ASP）规则集，同时保持效率和可扩展性。 FOLL-RM算法在性能方面具有竞争力，具有广泛使用的最新算法，例如XGBoost和多层感知器（MLP）（MLP），但是，与这些算法不同，Fold-RM算法会产生可解释的模型。 fold-rm在某些数据集（尤其是大型数据集）上的表现优于XGBoost。 fold-rm还为预测提供了对人类友好的解释。
FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for multi-category classification tasks while maintaining efficiency and scalability. The FOLD-RM algorithm is competitive in performance with the widely-used, state-of-the-art algorithms such as XGBoost and multi-layer perceptrons (MLPs), however, unlike these algorithms, the FOLD-RM algorithm produces an explainable model. FOLD-RM outperforms XGBoost on some datasets, particularly large ones. FOLD-RM also provides human-friendly explanations for predictions.
