Adapting performance metrics for ordinal classification to interval scale : length matters
Delgado de la Torre, Rosario (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Binotto, Giulia (Universitat Autònoma de Barcelona. Departament de Matemàtiques)

Data:	2025
Resum:	In the field of supervised machine learning, accurate evaluation of classification models is a critical factor for assessing their performance and guiding model selection. This paper delves into the domain of ordinal classification and raises the question of adapting ordinal metrics to the interval scale. In scenarios where measurements are recorded at intervals, not only the order but also their length assume significance, and this promotes the adoption of novel performance metrics. Initially, we revisit two existing confusion matrix-based ordinal metrics and introduce a normalization technique to render them comparable and enhance their practical utility. We extend our focus to classification by intervals, proposing a robust framework for adapting ordinal metrics to the interval scale, and applying it to the aforementioned ordinal metrics. We address the challenge of unbounded rightmost intervals, a common issue in practical applications, from both theoretical and simulation perspectives, by providing a solution that enhances the applicability of the proposed metrics. To further explore practical implications, we conducted experiments on real-world datasets. The results reveal a promising trend in the use of interval-scale metrics to guide hyper-parameter tuning for improving model performance.
Ajuts:	Agencia Estatal de Investigación PID2021-123733NB-I00
Nota:	Altres ajuts: acords transformatius de la UAB
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Cost-sensitive metrics ; Hyper-parameter tuning ; Interval-scale classification ; Ordinal classification ; Performance metrics
Publicat a:	Machine learning (Dordrech), Vol. 114 (January 2025) , art. 41, ISSN 1573-0565

DOI: 10.1007/s10994-024-06654-4

49 p, 3.3 MB

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