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Iterative ranking aggregation using quality improvement of subgroup ranking

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  • Ding, Jiankun
  • Han, Deqiang
  • Yang, Yi

Abstract

Ranking aggregation concerns the combination of rankings to obtain a consensus ranking that best represents all the input rankings according to a specific criterion. However, finding the optimal aggregated ranking, i.e., the one with the highest quality, is usually NP-hard. To reduce the computational cost, many heuristic aggregation methods have been proposed. They cannot ensure the optimality and the qualities of aggregated rankings obtained actually can be further improved. To find a better aggregated ranking, a novel iterative ranking aggregation method (IRAM) is proposed in this paper using the quality improvement of the subgroup ranking. IRAM starts from an aggregated ranking generated by a traditional heuristic ranking aggregation method. In each iteration step, IRAM attempts to improve the ranking’s quality of a subgroup of items. If the ranking’s quality of a subgroup of items is improved, the full aggregated ranking’s quality is consequently improved. Moreover, a window iterative ranking aggregation method (W-IRAM) is designed, which is simpler than the IRAM. We prove that IRAM and W-IRAM bring better (or at least the same) aggregation qualities compared with the traditional heuristic ranking aggregation methods. Simulation results show that our iterative ranking aggregation approaches perform well.

Suggested Citation

  • Ding, Jiankun & Han, Deqiang & Yang, Yi, 2018. "Iterative ranking aggregation using quality improvement of subgroup ranking," European Journal of Operational Research, Elsevier, vol. 268(2), pages 596-612.
    • Handle: RePEc:eee:ejores:v:268:y:2018:i:2:p:596-612
    DOI: 10.1016/j.ejor.2018.01.031
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    References listed on IDEAS

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    Cited by:

    1. Akbari, Sina & Escobedo, Adolfo R., 2023. "Beyond kemeny rank aggregation: A parameterizable-penalty framework for robust ranking aggregation with ties," Omega, Elsevier, vol. 119(C).
    2. Montes, Ignacio & Rademaker, Michael & Pérez-Fernández, Raúl & De Baets, Bernard, 2020. "A correspondence between voting procedures and stochastic orderings," European Journal of Operational Research, Elsevier, vol. 285(3), pages 977-987.

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