Improving the classification of extreme classes by means of loss regularisation and generalised beta distributions

TL;DR

This paper introduces a unimodal regularisation method using generalized beta distributions to enhance the classification accuracy of extreme classes in ordinal problems, demonstrating superior performance across multiple datasets.

Contribution

The authors propose a novel unimodal regularisation approach applicable to any loss function, improving extreme class classification in ordinal tasks with comprehensive experimental validation.

Findings

Improves classification of extreme classes in ordinal datasets

Generalized beta distribution enhances performance in extreme classes

Method maintains good overall classification performance

Abstract

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.