Using metaheuristic algorithms for parameter estimation in generalized Mallows models
•We deal with the problem of parameter estimation in Generalized Mallows model (GMM).•We deal with 22 real datasets, all of them but one created by the authors.•We have designed two experiments varying the maximum evaluations allowed.•Obtained results significantly improve the previous competing app...
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| Veröffentlicht in: | Applied soft computing Jg. 38; S. 308 - 320 |
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| Abstract | •We deal with the problem of parameter estimation in Generalized Mallows model (GMM).•We deal with 22 real datasets, all of them but one created by the authors.•We have designed two experiments varying the maximum evaluations allowed.•Obtained results significantly improve the previous competing approaches.
This paper deals with the problem of parameter estimation in the generalized Mallows model (GMM) by using both local and global search metaheuristic (MH) algorithms. The task we undertake is to learn parameters for defining the GMM from a dataset of complete rankings/permutations. Several approaches can be found in the literature, some of which are based on greedy search and branch and bound search. The greedy approach has the disadvantage of usually becoming trapped in local optima, while the branch and bound approach, basically A* search, usually comes down to approximate search because of memory requirements, losing in this way its guaranteed optimality. Here, we carry out a comparative study of several MH algorithms (iterated local search (ILS) methods, variable neighborhood search (VNS) methods, genetic algorithms (GAs) and estimation of distribution algorithms (EDAs)) and a tailored algorithm A* to address parameter estimation in GMMs. We use 22 real datasets of different complexity, all but one of which were created by the authors by preprocessing real raw data. We provide a complete analysis of the experiments in terms of accuracy, number of iterations and CPU time requirements. |
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| AbstractList | •We deal with the problem of parameter estimation in Generalized Mallows model (GMM).•We deal with 22 real datasets, all of them but one created by the authors.•We have designed two experiments varying the maximum evaluations allowed.•Obtained results significantly improve the previous competing approaches.
This paper deals with the problem of parameter estimation in the generalized Mallows model (GMM) by using both local and global search metaheuristic (MH) algorithms. The task we undertake is to learn parameters for defining the GMM from a dataset of complete rankings/permutations. Several approaches can be found in the literature, some of which are based on greedy search and branch and bound search. The greedy approach has the disadvantage of usually becoming trapped in local optima, while the branch and bound approach, basically A* search, usually comes down to approximate search because of memory requirements, losing in this way its guaranteed optimality. Here, we carry out a comparative study of several MH algorithms (iterated local search (ILS) methods, variable neighborhood search (VNS) methods, genetic algorithms (GAs) and estimation of distribution algorithms (EDAs)) and a tailored algorithm A* to address parameter estimation in GMMs. We use 22 real datasets of different complexity, all but one of which were created by the authors by preprocessing real raw data. We provide a complete analysis of the experiments in terms of accuracy, number of iterations and CPU time requirements. |
| Author | Aledo, Juan A. Gámez, Jose A. Molina, David |
| Author_xml | – sequence: 1 givenname: Juan A. surname: Aledo fullname: Aledo, Juan A. email: juanangel.aledo@uclm.es organization: Departamento de Matemáticas, Universidad de Castilla-La Mancha, Albacete 02071, Spain – sequence: 2 givenname: Jose A. surname: Gámez fullname: Gámez, Jose A. email: jose.gamez@uclm.es organization: Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete 02071, Spain – sequence: 3 givenname: David surname: Molina fullname: Molina, David email: david.molina@uclm.es organization: Departamento de Matemáticas, Universidad de Castilla-La Mancha, Albacete 02071, Spain |
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| CitedBy_id | crossref_primary_10_1016_j_asoc_2019_105761 crossref_primary_10_1016_j_ejor_2018_04_031 crossref_primary_10_1145_3206429 crossref_primary_10_1007_s10342_021_01355_2 crossref_primary_10_1016_j_amc_2016_06_005 crossref_primary_10_1016_j_eswa_2022_119131 crossref_primary_10_1007_s00521_016_2677_x crossref_primary_10_1016_j_cam_2018_03_014 crossref_primary_10_1016_j_cor_2016_09_013 crossref_primary_10_1016_j_asoc_2018_06_006 |
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