秩聚合的启发式搜索及其在标号排序中的应用
Heuristic Search for Rank Aggregation with Application to Label Ranking
摘要：
排名聚合旨在将来自不同投票者的多个备选方案的偏好排名组合成一个单一的共识排名。然而，作为一个有用的模型的各种实际应用，它是一个计算上具有挑战性的问题。在本文中，我们提出了一个有效的混合进化排序算法来解决的排名聚集问题的完全和部分的排名。该算法的特点是一个语义交叉的基础上和谐对和一个迟接受局部搜索加强了一个有效的增量评估技术。实验进行评估的算法，表明具有很强的竞争力的性能与国家的最先进的算法相比，在基准实例。为了证明其实用性，该算法被应用到标签排名，这是一个重要的机器学习任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
