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<p>The <i>partial label ranking</i> (PLR) problem is a supervised learning scenario that aims to fit a <i>preference model</i> that predicts a <i>bucket order</i> defined over a set of labels for a given input instance. This problem generalizes the well-known <i>label ranking</i> (LR) problem, which, in practice, is limited to outputting <i>total orders</i> of labels. Existing PLR methods have primarily extended LR approaches to handle <i>ties</i> in predictions. This paper proposes using <i>multi-output regression</i> to address the PLR problem, introducing an <i>encoder</i> that, during the learning phase, transforms the (possibly incomplete) <i>rankings with ties</i> of labels to multivariate regression targets, an underexplored perspective in both LR and PLR. Moreover, during the inference phase, we introduce several <i>post-hoc layers</i> that convert the MOR 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 PLR&#xa0;methods, as demonstrated through experimental evaluations.</p>

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

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Santo M. A. R. Thies,
  • Juan C. Alfaro,
  • Viktor Bengs

摘要

The partial label ranking (PLR) 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 (LR) problem, which, in practice, is limited to outputting total orders of labels. Existing PLR methods have primarily extended LR approaches to handle ties in predictions. This paper proposes using multi-output regression to address the PLR 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 LR and PLR. Moreover, during the inference phase, we introduce several post-hoc layers that convert the MOR 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 PLR methods, as demonstrated through experimental evaluations.