论文标题
使用应用标签排名的应用程序搜索等级聚合
Heuristic Search for Rank Aggregation with Application to Label Ranking
论文作者
论文摘要
等级汇总旨在将来自不同选民的许多替代方案的偏好排名结合到单个共识排名中。但是，作为各种实际应用的有用模型，这是一个具有挑战性的问题。在本文中，我们提出了一种有效的混合进化排名算法，以解决完整和部分排名的等级聚合问题。该算法具有基于一致对的语义交叉，并且通过有效的增量评估技术加强了较晚的局部搜索。进行实验以评估算法，表明与最先进的算法相比，基准实例的竞争性能高。为了证明其实用性，将算法应用于标签排名，这是一项重要的机器学习任务。
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.
