The \textit{label ranking} problem is a supervised learning scenario in which the learner predicts a \textit{total order} of the class labels for a given input instance. Recently, research has increasingly focused on the \textit{partial label ranking} problem, a generalization of the label ranking problem that allows \textit{ties} in the predicted orders. So far, most existing learning approaches for the partial label ranking problem rely on approximation algorithms for rank aggregation in the final prediction step. This paper explores several alternative aggregation methods for this critical step, including scoring-based and probabilistic-based rank aggregation approaches. To enhance their suitability for the more general partial label ranking problem, the investigated methods are extended to increase the likelihood of producing ties. Experimental evaluations on standard benchmarks demonstrate that scoring-based variants consistently outperform the current state-of-the-art method in handling incomplete information. In contrast, probabilistic-based variants fail to achieve competitive performance.

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