Code and Data Repository for Ranking Model Averaging: Ranking Based on Model Averaging
针对连续响应排序问题中评分函数不确定性的挑战，提出排序模型平均方法，通过K折交叉验证为候选模型分配权重，适用于葡萄酒质量排序等实际场景。
Ranking problems are commonly encountered in practical applications, including order priority ranking, wine quality ranking, and piston slap noise performance ranking. The responses of these ranking applications are often considered as continuous responses and there is uncertainty on which scoring function is used to model the responses. In this paper, we address the scoring function uncertainty of continuous response ranking problems by proposing a Ranking Model Averaging (RMA) method. With a set of candidate models varied by scoring functions, RMA assigns weights for each model determined by a K-fold cross-validation criterion based on pairwise loss.