序数决策树的分裂标准：一项实证研究
Splitting criteria for ordinal decision trees: an experimental study
AI总结：
本文通过实验证明序数分裂标准在决策树中的有效性，发现Ordinal Gini在减少均方误差方面表现最佳，同时提供了可复现的研究资源。
摘要：
序数分类（OC）处理具有自然顺序标签的分类任务。与名义分类不同，OC考虑顺序关系，产生更准确的结果。尽管如此，OC问题常使用名义方法，导致次优解。本文全面调研了序数分裂标准，标准化文献中的符号以提高清晰度。通过将三种序数分裂标准（Ordinal Gini, Weighted Information Gain, Ranking Impurity）与名义标准（Gini, Information Gain）纳入决策树分类器进行比较，展示了45个公开可用的OC数据集，并使用知名OC评估指标进行首次实验证明。结果经过统计分析，发现Ordinal Gini是目前最佳的序数分裂标准，将Gini的均方误差减少了超过3.02%。为促进可复现性，提供了所有开发的源代码、详细的复现指南、45个OC数据集以及所有评估方法的个体结果。
英文摘要：
Ordinal Classification (OC) addresses those classification tasks where the labels exhibit a natural order. Unlike nominal classification, which treats all classes as mutually exclusive and unordered, OC takes the ordinal relationship into account, producing more accurate and relevant results. This is particularly critical in applications where the magnitude of classification errors has significant consequences. Despite this, OC problems are often tackled using nominal methods, leading to suboptimal solutions. Although decision trees are among the most popular classification approaches, ordinal tree-based approaches have received less attention when compared to other classifiers. This work provides a comprehensive survey of ordinal splitting criteria, standardising the notations used in the literature to enhance clarity and consistency. Three ordinal splitting criteria, Ordinal Gini (OGini), Weighted Information Gain (WIG), and Ranking Impurity (RI), are compared to the nominal counterparts of the first two (Gini and information gain), by incorporating them into a decision tree classifier. An extensive repository considering $45$ publicly available OC datasets is presented, supporting the first experimental comparison of ordinal and nominal splitting criteria using well-known OC evaluation metrics. The results have been statistically analysed, highlighting that OGini stands out as the best ordinal splitting criterion to date, reducing the mean absolute error achieved by Gini by more than 3.02%. To promote reproducibility, all source code developed, a detailed guide for reproducing the results, the 45 OC datasets, and the individual results for all the evaluated methodologies are provided.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
