Heuristic Search for Rank Aggregation with Application to Label Ranking
This work addresses a practical problem in machine learning for applications like label ranking, but it is incremental as it builds on existing methods with specific improvements.
The paper tackles the computationally challenging rank aggregation problem by proposing a hybrid evolutionary ranking algorithm, which demonstrates highly competitive performance on benchmark instances compared to state-of-the-art algorithms.
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.