A comparative analysis of rank aggregation methods for the partial label ranking problem

TL;DR

This paper compares various rank aggregation methods for the partial label ranking problem, highlighting the effectiveness of scoring-based approaches over probabilistic ones in handling incomplete label information.

Contribution

It introduces and evaluates alternative rank aggregation methods, extending them to better handle ties in partial label ranking, and compares their performance on benchmark datasets.

Findings

Scoring-based aggregation methods outperform probabilistic ones.

Extended methods increase the likelihood of ties in predictions.

Scoring-based variants outperform state-of-the-art in benchmarks.

Abstract

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.