A comparative analysis of rank aggregation methods for the partial label ranking problem
This work addresses a generalization of the label ranking problem for machine learning applications, but it is incremental as it focuses on improving aggregation methods within an existing framework.
The paper tackled the partial label ranking problem by exploring alternative rank aggregation methods, finding that scoring-based variants consistently outperformed the state-of-the-art in handling incomplete information, while non-parametric probabilistic-based variants did not achieve competitive performance.
The label ranking problem is a supervised learning scenario in which the learner predicts a total order of the class labels for a given input instance. Recently, research has increasingly focused on the partial label ranking problem, a generalization of the label ranking problem that allows 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 non-parametric 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, non-parametric probabilistic-based variants fail to achieve competitive performance.