  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.
