MORE-PLR：多输出回归用于部分标签排序
MORE-PLR: multi-output regression employed for partial label ranking
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中文总结 AI 辅助
本文提出MORE-PLR，一种基于多输出回归的部分标签排序方法，通过编码器将带并列的排序转化为回归目标，并在推理阶段用后处理层生成桶序，实验证明其性能与现有最先进方法相当。
中文摘要 AI 辅助
部分标签排序问题是一种监督学习场景，旨在拟合一个偏好模型，该模型针对给定的输入实例预测定义在标签集合上的桶序。该问题推广了众所周知的标签排序问题，后者在实践中仅限于输出标签的全序。现有的部分标签排序方法主要扩展了标签排序方法以处理预测中的并列情况。本文提出使用多输出回归来解决部分标签排序问题，引入了一个编码器，在学习阶段将（可能不完整的）带并列的标签排序转换为多变量回归目标，这是标签排序和部分标签排序中一个未被充分探索的视角。此外，在推理阶段，我们引入了几个后处理层，将多输出回归结果转换为输出桶序，以有效实现该方法。实验评估表明，该框架提供的学习策略与当前最先进的部分标签排序方法具有竞争力。
英文摘要
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.
发表机构
- Ludwig-Maximilians-Universität München（慕尼黑大学）
- Universidad de Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）
- Munich Center for Machine Learning（慕尼黑机器学习中心）
机构由 AI 辅助整理，请以论文原文为准。
