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DoME: A Deterministic Technique for Equation Development and Symbolic Regression
dc.contributor.author | Rivero, Daniel | |
dc.contributor.author | Fernández-Blanco, Enrique | |
dc.contributor.author | Pazos, A. | |
dc.date.accessioned | 2022-03-17T10:18:18Z | |
dc.date.available | 2022-03-17T10:18:18Z | |
dc.date.issued | 2022-03-04 | |
dc.identifier.citation | Daniel Rivero, Enrique Fernandez-Blanco, Alejandro Pazos, DoME: A deterministic technique for equation development and Symbolic Regression, Expert Systems with Applications, Volume 198, 2022, 116712, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.116712. (https://www.sciencedirect.com/science/article/pii/S0957417422001889) | |
dc.identifier.issn | 0957-4174 | |
dc.identifier.uri | http://hdl.handle.net/2183/30038 | |
dc.description | Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG | |
dc.description.abstract | [Abstract] Based on a solid mathematical background, this paper proposes a method for Symbolic Regression that enables the extraction of mathematical expressions from a dataset. Contrary to other approaches, such as Genetic Programming, the proposed method is deterministic and, consequently, does not require the creation of a population of initial solutions. Instead, a simple expression is grown until it fits the data. This method has been compared with four well-known Symbolic Regression techniques with a large number of datasets. As a result, on average, the proposed method returns better performance than the other techniques, with the advantage of returning mathematical expressions that can be easily used by different systems. Additionally, this method makes it possible to establish a threshold at the complexity of the expressions generated, i.e., the system can return mathematical expressions that are easily analyzed by the user, as opposed to other techniques that return very large expressions. | es_ES |
dc.description.sponsorship | This study is partially supported by Instituto de Salud Carlos III, grant number PI17/01826 (Collaborative Project in Genomic Data Integration (CICLOGEN) funded by the Instituto de Salud Carlos III from the Spanish National Plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe”. It was also partially supported by different grants and projects from the Xunta de Galicia [ED431D 2017/23; ED431D 2017/16; ED431G/01; ED431C 2018/49; IN845D-2020/03]. The authors thank the CyTED, Spain and each National Organism for Science and Technology for funding the IBEROBDIA project (P918PTE0409). In this regard, Spain specifically thanks the Ministry of Economy and Competitiveness for the financial support for this project through the State Program of I+D+I Oriented to the Challenges of Society 2017–2020 (International Joint Programming 2018), project (PCI2018-093284). Funding for open access charge: Universidade da Coruña/CISUG | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/23 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/16 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2018/49 | es_ES |
dc.description.sponsorship | Xunta de Galicia; IN845D-2020/03 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016/PI17%2F01826/ES/PROYECTO COLABORATIVO DE INTEGRACION DE DATOS GENOMICOS (CICLOGEN). TECNICAS DE DATA MINING Y DOCKING MOLECULAR PARA ANALISIS DE DATOS INTEGRATIVOS EN CANCER DE COLON/ | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PCI2018-093284/ES/OBESIDAD Y DIABETES EN IBEROAMERICA: FACTORES DE RIESGO Y NUEVOS BIOMARCADORES PATOGENICOS Y PREDICTIVOS/ | |
dc.relation.uri | https://doi-org.accedys.udc.es/10.1016/j.eswa.2022.116712 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Symbolic regression | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Artificial intelligence | es_ES |
dc.title | DoME: A Deterministic Technique for Equation Development and Symbolic Regression | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Expert Systems with Applications | es_ES |
UDC.volume | 198 | es_ES |
UDC.startPage | 116712 | es_ES |
dc.identifier.doi | 10.1016/j.eswa.2022.116712 |
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