Cautious Label-Wise Ranking with Constraint Satisfaction

Abstract

Ranking problems are difficult to solve due to their combinatorial nature. One way to solve this issue is to adopt a decomposition scheme, splitting the initial difficult problem in many simpler problems. The predictions obtained from these simplified settings must then be combined into one single output, possibly resolving inconsistencies between the outputs. In this paper, we consider such an approach for the label ranking problem, where in addition we allow the predictive model to produce cautious inferences in the form of sets of rankings when it lacks information to produce reliable, precise predictions. More specifically, we propose to combine a rank-wise decomposition, in which every sub-problem becomes an ordinal classification one, with a constraint satisfaction problem (CSP) approach to verify the consistency of the predictions. Our experimental results indicate that our approach produces predictions with appropriately balanced reliability and precision, while remaining competitive with classical, precise approaches.

BoostLR: A Boosting-Based Learning Ensemble for Label Ranking Tasks

“…(e.g. [10], [13], [15], [20], [35]). Another common technique modifies existing probabilistic algorithms so that they can support label ranking.…”

Inferring from an imprecise Plackett–Luce model: Application to label ranking

“…As suggested by an anonymous reviewer, it might be interesting to consider alternatives estimation methods such as epsilon contamination. There already exist non-parametric, decomposition-based approaches to label ranking with imprecise ranks; see [11,4]. However, the PL model, being tied to an order representation, may not be well-suited to such an approach.…”