{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:27:39Z","timestamp":1776680859780,"version":"3.51.2"},"reference-count":37,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2021,8,5]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>This paper presents an algorithm that can elicitate (infer) all or any combination of elimination and choice expressing reality (ELECTRE) Tri-B parameters. For example, a decision maker can maintain the values for indifference, preference and veto thresholds, and the study\u2019s algorithm can find the criteria weights, reference profiles and the lambda cutting level. The study\u2019s approach is inspired by a machine learning ensemble technique, the random forest, and for that, the authors named the study\u2019s approach as ELECTRE tree algorithm.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>First, the authors generate a set of ELECTRE Tri-B models, where each model solves a random sample of criteria and alternates. Each sample is made with replacement, having at least two criteria and between 10% and 25% of alternates. Each model has its parameters optimized by a genetic algorithm (GA) that can use an ordered cluster or an assignment example as a reference to the optimization. Finally, after the optimization phase, two procedures can be performed; the first one will merge all models, finding in this way the elicitated parameters and in the second procedure, each alternate is classified (voted) by each separated model, and the majority vote decides the final class.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The authors have noted that concerning the voting procedure, nonlinear decision boundaries are generated and they can be suitable in analyzing problems of the same nature. In contrast, the merged model generates linear decision boundaries.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The elicitation of ELECTRE Tri-B parameters is made by an ensemble technique that is composed of a set of multicriteria models that are engaged in generating robust solutions.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-10-2020-0256","type":"journal-article","created":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T21:26:51Z","timestamp":1628026011000},"page":"586-608","source":"Crossref","is-referenced-by-count":6,"title":["ELECTRE tree: a machine learning approach to infer ELECTRE Tri-B parameters"],"prefix":"10.1108","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4526-1485","authenticated-orcid":false,"given":"Gabriela Montenegro","family":"Montenegro de Barros","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0599-8888","authenticated-orcid":false,"given":"Valdecy","family":"Pereira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2398-5207","authenticated-orcid":false,"given":"Marcos Costa","family":"Roboredo","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"key2022092811513014400_ref001","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1145\/1283383.1283494","article-title":"K-means++: the advantages of careful seeding","year":"2007"},{"key":"key2022092811513014400_ref002","doi-asserted-by":"publisher","article-title":"A genetic algorithms tutorial tool for numerical function optimisation","year":"1997","DOI":"10.1145\/268819.268830"},{"key":"key2022092811513014400_ref003","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.ejor.2012.05.032","article-title":"Eliciting ELECTRE TRI category limits for a group of decision makers","volume":"223","year":"2012","journal-title":"European Journal of Operational Research"},{"issue":"2","key":"key2022092811513014400_ref004","first-page":"115","article-title":"Simulated binary crossover for continuous search space","volume":"9","year":"1995","journal-title":"Complex Systems"},{"key":"key2022092811513014400_ref005","volume-title":"Inferring ELECTRE's Veto Related Parameters from Outranking Examples","year":"2002"},{"key":"key2022092811513014400_ref006","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s12187-019-09715-6","article-title":"Dependence Analysis between childhood social indicators and human development index through canonical correlation analysis","volume":"13","year":"2020","journal-title":"Child Indicators Research"},{"key":"key2022092811513014400_ref007","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1016\/j.ejor.2008.11.035","article-title":"An evolutionary approach to construction of outranking models for multicriteria classification: the case of the ELECTRE TRI method","volume":"199","year":"2009","journal-title":"European Journal of Operational Research"},{"key":"key2022092811513014400_ref008","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1016\/j.asoc.2019.01.050","article-title":"An indirect elicitation method for the parameters of the ELECTRE TRI-nB model using genetic algorithms","volume":"77","year":"2019","journal-title":"Applied Soft Computing"},{"key":"key2022092811513014400_ref009","doi-asserted-by":"crossref","unstructured":"Figueira, J., Roy, B. and Mousseau, V. 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