dc.creator | García Nieto, José Manuel | es |
dc.creator | Alba, Enrique | es |
dc.creator | Jourdan, Laetitia | es |
dc.creator | Talbi, El-Ghazali | es |
dc.date.accessioned | 2021-05-13T10:14:25Z | |
dc.date.available | 2021-05-13T10:14:25Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | García Nieto, J.M., Alba, E., Jourdan, L. y Talbi, E. (2009). Sensitivity and specificity based multiobjective approach for feature selection: Application to cancer diagnosis. Information Processing Letters, 109 (16), 887-896. | |
dc.identifier.issn | 0020-0190 | es |
dc.identifier.uri | https://hdl.handle.net/11441/109000 | |
dc.description.abstract | The study of the sensitivity and the specificity of a classification test constitute a powerful
kind of analysis since it provides specialists with very detailed information useful for
cancer diagnosis. In this work, we propose the use of a multiobjective genetic algorithm
for gene selection of Microarray datasets. This algorithm performs gene selection from
the point of view of the sensitivity and the specificity, both used as quality indicators
of the classification test applied to the previously selected genes. In this algorithm, the
classification task is accomplished by Support Vector Machines; in addition a 10-Fold Cross-
Validation is applied to the resulting subsets. The emerging behavior of all these techniques
used together is noticeable, since this approach is able to offer, in an original and easy
way, a wide range of accurate solutions to professionals in this area. The effectiveness
of this approach is proved on public cancer datasets by working out new and promising
results. A comparative analysis of our approach using two and three objectives, and with
other existing algorithms, suggest that our proposal is highly appropriate for solving this
problem. | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación TIN2008-06491-C04-01 | es |
dc.description.sponsorship | Junta de Andalucía P07-TIC-03044 | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Information Processing Letters, 109 (16), 887-896. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Algorithms | es |
dc.subject | Analysis of algorithms | es |
dc.subject | Combinatorial problems | es |
dc.subject | Databases | es |
dc.subject | Design of algorithms | es |
dc.subject | Performance evaluation | es |
dc.subject | Sensitivity and specificity analysis | es |
dc.subject | Multiobjective genetic algorithm | es |
dc.subject | Microarray gene selection | es |
dc.title | Sensitivity and specificity based multiobjective approach for feature selection: Application to cancer diagnosis | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2008-06491-C04-01 | es |
dc.relation.projectID | P07-TIC-03044 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0020019009001264 | es |
dc.identifier.doi | 10.1016/j.ipl.2009.03.029 | es |
dc.journaltitle | Information Processing Letters | es |
dc.publication.volumen | 109 | es |
dc.publication.issue | 16 | es |
dc.publication.initialPage | 887 | es |
dc.publication.endPage | 896 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Junta de Andalucía | es |