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Heuristic Search for Rank Aggregation with Application to Label Ranking

Author

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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)

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.

Suggested Citation

  • Yangming Zhou & Jin-Kao Hao & Zhen Li, 2024. "Heuristic Search for Rank Aggregation with Application to Label Ranking," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 308-326, March.
    • Handle: RePEc:inm:orijoc:v:36:y:2024:i:2:p:308-326
    DOI: 10.1287/ijoc.2022.0019
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    References listed on IDEAS

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