Problem
Research questions and friction points this paper is trying to address.
partial label ranking
bucket order
supervised learning
label ranking
MORE-PLR: multi-output regression employed for partial label ranking
🤖 AI Summary
This study addresses the limitations of existing partial label ranking methods that naively extend total order models and struggle to efficiently handle label ties. For the first time, this work reformulates the task from a multi-output regression perspective. Methodologically, it introduces a tailored encoder that transforms partial orders with ties into regression targets for preference modeling, coupled with a post-processing layer during inference to generate bucket order predictions. This framework effectively resolves the challenge of label ties through its unique encoding-decoding mechanism. Experiments demonstrate that the proposed approach achieves state-of-the-art performance across multiple benchmark datasets, validating the effectiveness of the regression paradigm 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Partial Label Ranking
Multi-output Regression
Bucket Order
Encoder
Post-hoc Layers
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