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Do all roads lead to Rome? Studying distance measures in the context of machine learning
dc.contributor.author | Blanco Mallo, Eva | |
dc.contributor.author | Morán-Fernández, Laura | |
dc.contributor.author | Remeseiro, Beatriz | |
dc.contributor.author | Bolón-Canedo, Verónica | |
dc.date.accessioned | 2024-07-02T09:52:36Z | |
dc.date.available | 2024-07-02T09:52:36Z | |
dc.date.issued | 2023-09 | |
dc.identifier.citation | E. Blanco-Mallo, L. Morán-Fernández, B. Remeseiro, V. Bolón-Canedo, "Do all roads lead to Rome? Studying distance measures in the context of machine learning", Pattern Recognition, Vol. 141, Sept. 2023, article number 109646, doi: 10.1016/j.patcog.2023.109646 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/37618 | |
dc.description.abstract | [Abstract]: Many machine learning and data mining tasks are based on distance measures, so a large amount of literature addresses this aspect somehow. Due to the broad scope of the topic, this paper aims to provide an overview of the use of these measures in the most common machine learning problems, pointing out those aspects to consider to choose the most appropriate measure for a particular task. For this purpose, the most recent works addressing the subject were reviewed and seven of the most commonly used measures were analyzed, investigating in detail their main properties and applications. Different experiments were carried out to study their relationships and compare their performance. The degradation of the results in the presence of noise was also considered, as well as the execution time required by each measure. | es_ES |
dc.description.sponsorship | This work has been supported by the National Plan for Scientific and Technical Research and Innovation of the Spanish Government (Grant PID2019-109238GB, subprojects C21 and C22), by the Spanish Ministry of Science and Innovation (Grant FPI PRE2020-092608), and by the Xunta de Galicia (Grant ED431C 2022/44) with the European Union ERDF funds. CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidades from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2022/44 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier Ltd | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C21/ES/SISTEMAS DE RECOMENDACION EXPLICABLES | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C22/ES/APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLE | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PRE2020-092608/ES/ | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.patcog.2023.109646 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Classification | es_ES |
dc.subject | Clustering | es_ES |
dc.subject | Distance measures | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Similarity measures | es_ES |
dc.title | Do all roads lead to Rome? Studying distance measures in the context of machine learning | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Pattern Recognition | es_ES |
UDC.volume | 141 | es_ES |
UDC.startPage | 109646 | es_ES |
dc.identifier.doi | 10.1016/j.patcog.2023.109646 |
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