Heuristic Search for Rank Aggregation with Application to Label Ranking

TL;DR

This paper introduces a hybrid evolutionary algorithm for rank aggregation that effectively combines preferences from multiple voters, demonstrating strong performance on benchmarks and practical application to label ranking tasks.

Contribution

It presents a novel hybrid evolutionary ranking algorithm with semantic crossover and local search, improving efficiency and effectiveness in rank aggregation and label ranking.

Findings

Competitive performance on benchmark instances

Effective in handling complete and partial rankings

Successfully applied to label ranking in machine learning

Abstract

Rank aggregation aims to combine the preference rankings of a number of alternatives from different voters into a single consensus ranking. As a useful model for a variety of practical applications, however, it is a computationally challenging problem. In this paper, we propose an effective hybrid evolutionary ranking algorithm to solve the rank aggregation problem with both complete and partial rankings. The algorithm features a semantic crossover based on concordant pairs and a late acceptance local search reinforced by an efficient incremental evaluation technique. Experiments are conducted to assess the algorithm, indicating a highly competitive performance on benchmark instances compared with state-of-the-art algorithms. To demonstrate its practical usefulness, the algorithm is applied to label ranking, which is an important machine learning task.