An ordinal classification problem is one in which the target variable takes values on an ordinal scale. Nowadays, there are many of these problems associated with real-world tasks where it is crucial to accurately classify the extreme classes of the ordinal structure. In this work, we propose a unimodal regularisation approach that can be applied to any loss function to improve the classification performance of the first and last classes while maintaining good performance for the remainder. The proposed methodology is tested on six datasets with different numbers of classes, and compared with other unimodal regularisation methods in the literature. In addition, performance in the extreme classes is compared using a new metric that takes into account their sensitivities. Experimental results and statistical analysis show that the proposed methodology obtains a superior average performance considering different metrics. The results for the proposed metric show that the generalised beta distribution generally improves classification performance in the extreme classes. At the same time, the other five nominal and ordinal metrics considered show that the overall performance is aligned with the performance of previous alternatives.

翻译：序数分类问题是指目标变量取值于序数尺度的一类分类任务。当前，众多现实任务涉及此类问题，其中准确分类序数结构的极端类别至关重要。本研究提出一种单峰正则化方法，该方法可应用于任意损失函数，在保持其他类别良好分类性能的同时，显著提升首尾极端类别的分类表现。所提方法在六个具有不同类别数量的数据集上进行验证，并与文献中其他单峰正则化方法进行比较。此外，通过引入考虑极端类别敏感度的新评估指标，对极端类别性能进行量化对比。实验结果表明：在不同评估指标下，所提方法均取得更优的平均性能；针对新指标的分析显示，广义Beta分布在多数情况下能有效提升极端类别的分类性能；同时，其余五个名义及序数评估指标表明，该方法的整体性能与现有替代方案保持相当水平。