Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms
Multicriteria sorting involves assigning the objects of decisions (actions) into <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> priori known ordered classes considering the preferences of a decision maker (DM). Two new multicriteria sor...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10005265/ |
| _version_ | 1797807282748129280 |
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| author | Eduardo Fernandez Jorge Navarro Efrain Solares Carlos A. Coello Coello Raymundo Diaz Abril Flores |
| author_facet | Eduardo Fernandez Jorge Navarro Efrain Solares Carlos A. Coello Coello Raymundo Diaz Abril Flores |
| author_sort | Eduardo Fernandez |
| collection | DOAJ |
| description | Multicriteria sorting involves assigning the objects of decisions (actions) into <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> priori known ordered classes considering the preferences of a decision maker (DM). Two new multicriteria sorting methods were recently proposed by the authors. These methods are based on a novel approach called interval-based outranking which provides the methods with attractive practical and theoretical characteristics. However, as is well known, defining parameter values for methods based on the outranking approach is often very difficult. This difficulty arises not only from the large number of parameters and the DM’s lack of familiarity with them, but also from imperfectly known (even missing) information. Here, we address: i) how to elicit the parameter values of the two new methods, and ii) how to incorporate imperfect knowledge during the elicitation. We follow the preference disaggregation paradigm and use evolutionary algorithms to address it. Our proposal performs very well in a wide range of computational experiments. Interesting findings are: i) the method restores the assignment examples with high effectiveness using only three profiles in each limiting boundary or representative actions per class; and ii) the ability to appropriately assign unknown actions can be greatly improved by increasing the number of limiting profiles. |
| first_indexed | 2024-03-13T06:20:14Z |
| format | Article |
| id | doaj.art-f913e25aa9f946118cb1c0b8abf0f857 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T06:20:14Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f913e25aa9f946118cb1c0b8abf0f8572023-06-09T23:00:22ZengIEEEIEEE Access2169-35362023-01-01113044306110.1109/ACCESS.2023.323424010005265Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary AlgorithmsEduardo Fernandez0Jorge Navarro1Efrain Solares2https://orcid.org/0000-0003-1310-8638Carlos A. Coello Coello3https://orcid.org/0000-0002-8435-680XRaymundo Diaz4Abril Flores5https://orcid.org/0000-0002-1197-8376Facultad de Contaduría y Administración, Universidad Autónoma de Coahuila, Torreon, MexicoFacultad de Informática, Universidad Autónoma de Sinaloa, Culiacán, MexicoFacultad de Contaduría y Administración, Universidad Autónoma de Coahuila, Torreon, MexicoCentro de Investigación y de Estudios Avanzados del IPN, Departmento de Computación, Ciudad de México, MexicoTecnológico de Monterrey, Monterrey, MexicoFacultad de Contaduría y Administración, Universidad Autónoma de Coahuila, Torreon, MexicoMulticriteria sorting involves assigning the objects of decisions (actions) into <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> priori known ordered classes considering the preferences of a decision maker (DM). Two new multicriteria sorting methods were recently proposed by the authors. These methods are based on a novel approach called interval-based outranking which provides the methods with attractive practical and theoretical characteristics. However, as is well known, defining parameter values for methods based on the outranking approach is often very difficult. This difficulty arises not only from the large number of parameters and the DM’s lack of familiarity with them, but also from imperfectly known (even missing) information. Here, we address: i) how to elicit the parameter values of the two new methods, and ii) how to incorporate imperfect knowledge during the elicitation. We follow the preference disaggregation paradigm and use evolutionary algorithms to address it. Our proposal performs very well in a wide range of computational experiments. Interesting findings are: i) the method restores the assignment examples with high effectiveness using only three profiles in each limiting boundary or representative actions per class; and ii) the ability to appropriately assign unknown actions can be greatly improved by increasing the number of limiting profiles.https://ieeexplore.ieee.org/document/10005265/Evolutionary algorithmsimperfect informationmultiple criteria analysismultiple criteria ordinal classificationoutranking methods |
| spellingShingle | Eduardo Fernandez Jorge Navarro Efrain Solares Carlos A. Coello Coello Raymundo Diaz Abril Flores Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms IEEE Access Evolutionary algorithms imperfect information multiple criteria analysis multiple criteria ordinal classification outranking methods |
| title | Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms |
| title_full | Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms |
| title_fullStr | Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms |
| title_full_unstemmed | Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms |
| title_short | Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms |
| title_sort | inferring preferences for multi criteria ordinal classification methods using evolutionary algorithms |
| topic | Evolutionary algorithms imperfect information multiple criteria analysis multiple criteria ordinal classification outranking methods |
| url | https://ieeexplore.ieee.org/document/10005265/ |
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