Learning decision trees for the partial label ranking problem

International Journal of Intelligent SystemsVolume 36, Issue 2 p. 890-918 RESEARCH ARTICLE Learning decision trees for the partial label ranking problem Juan C. Alfaro, Corresponding Author Juan C. Alfaro JuanCarlos.Alfaro@uclm.es orcid.org/0000-0003-1777-8540 Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Ciudad Real, Spain Laboratorio de Sistemas Inteligentes y Minería de Datos, Universidad de Castilla-La Mancha, Albacete, Spain Correspondence Juan C. Alfaro, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Ciudad Real 02071, Spain. Email: JuanCarlos.Alfaro@uclm.esSearch for more papers by this authorJuan A. Aledo, Juan A. Aledo orcid.org/0000-0003-1786-8087 Laboratorio de Sistemas Inteligentes y Minería de Datos, Universidad de Castilla-La Mancha, Albacete, Spain Departamento de Matemáticas, Universidad de Castilla-La Mancha, Ciudad Real, SpainSearch for more papers by this authorJosé A. Gámez, José A. Gámez orcid.org/0000-0003-1188-1117 Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Ciudad Real, Spain Laboratorio de Sistemas Inteligentes y Minería de Datos, Universidad de Castilla-La Mancha, Albacete, SpainSearch for more papers by this author Juan C. Alfaro, Corresponding Author Juan C. Alfaro JuanCarlos.Alfaro@uclm.es orcid.org/0000-0003-1777-8540 Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Ciudad Real, Spain Laboratorio de Sistemas Inteligentes y Minería de Datos, Universidad de Castilla-La Mancha, Albacete, Spain Correspondence Juan C. Alfaro, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Ciudad Real 02071, Spain. Email: JuanCarlos.Alfaro@uclm.esSearch for more papers by this authorJuan A. Aledo, Juan A. Aledo orcid.org/0000-0003-1786-8087 Laboratorio de Sistemas Inteligentes y Minería de Datos, Universidad de Castilla-La Mancha, Albacete, Spain Departamento de Matemáticas, Universidad de Castilla-La Mancha, Ciudad Real, SpainSearch for more papers by this authorJosé A. Gámez, José A. Gámez orcid.org/0000-0003-1188-1117 Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Ciudad Real, Spain Laboratorio de Sistemas Inteligentes y Minería de Datos, Universidad de Castilla-La Mancha, Albacete, SpainSearch for more papers by this author First published: 11 November 2020 https://doi.org/10.1002/int.22325Citations: 7Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The Label Ranking (LR) problem is a well-known nonstandard supervised classification problem, the goal of which is to learn preference classifiers from data, mapping instances to rankings of the labels of the class variable. In the literature, the particular setting where the output of the LR problem is a complete ranking without ties (a.k.a. permutation) has been profusely studied, and many algorithms have been designed to solve these particular instances based on the use of specific probability distributions and aggregation methods for permutations. However, also partial orders (a.k.a. bucket orders) can be considered as output in LR problems (i.e., some labels of the class variable may be tied), but the algorithms available do not tackle this kind of ranking. We refer to this particular case of LR as the Partial Label Ranking (PLR) problem. Thus, motivated by the lack of current methods to deal with the PLR problem, we design machine learning algorithms based on instance-based and decision tree approaches to tackle the PLR problem. We evaluate our proposals on a benchmark of 15 data sets obtained by transforming multiclass instances, and analyze their performance by carrying out a standard machine learning statistical analysis procedure. Citing Literature Volume36, Issue2February 2021Pages 890-918 RelatedInformation