MORE-PLR: multi-output regression employed for partial label ranking

Abstract:The partial label ranking problem is a supervised learning scenario that aims to fit a preference model that predicts a bucket order defined over a set of labels for a given input instance. This problem generalizes the well-known label ranking problem, which, in practice, is limited to outputting total orders of labels. Existing partial label ranking methods have primarily extended label ranking approaches to handle ties in predictions. This paper proposes using multi-output regression to address the partial label ranking problem, introducing an encoder that, during the learning phase, transforms the (possibly incomplete) rankings with ties of labels to multivariate regression targets, an underexplored perspective in both label ranking and partial label ranking. Moreover, during the inference phase, we introduce several post-hoc layers that convert the multi-output regression results into the output bucket order to effectively implement this approach. This framework provides learning strategies that are competitive with the current state-of-the-art partial label ranking methods, as demonstrated through experimental evaluations.

Code available at this https URL. Extended version of a paper presented at Discovery Science 2024

Machine Learning (cs.LG)

arXiv:2609.29386 [cs.LG]

(or arXiv:2609.29386v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.29386 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1007/s10994-025-06913-y Focus to learn more DOI(s) linking to related resources

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