Heuristic Search for Rank Aggregation with Application to Label Ranking

Yangming Zhou (), Jin-Kao Hao () and Zhen Li ()
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Yangming Zhou: Sino-US Global Logistics Institute, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China; Data-Driven Management Decision Making Lab, Shanghai Jiao Tong University, Shanghai 200030, China
Jin-Kao Hao: Department of Computer Science, Université d’Angers, Angers 49045, France
Zhen Li: Tencent Technology (Shanghai) Company Limited, Shanghai 200233, China

INFORMS Journal on Computing, 2024, vol. 36, issue 2, 308-326

Abstract: Rank aggregation combines the preference rankings of multiple alternatives from different voters into a single consensus ranking, providing a useful model for a variety of practical applications but posing a computationally challenging problem. In this paper, we provide 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 an enhanced late acceptance local search method reinforced by a relaxed acceptance and replacement strategy and a fast incremental evaluation mechanism. Experiments are conducted to assess the algorithm, indicating a highly competitive performance on both synthetic and real-world benchmark instances compared with state-of-the-art algorithms. To demonstrate its practical usefulness, the algorithm is applied to label ranking, a well-established machine learning task. We additionally analyze several key algorithmic components to gain insight into their operation.

Keywords: rank aggregation; label ranking; machine learning; evolutionary computation; metaheuristics (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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