Abstract
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.
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