dc.creator | Blanquero Bravo, Rafael | es |
dc.creator | Carrizosa Priego, Emilio José | es |
dc.creator | Ramírez Cobo, Josefa | es |
dc.creator | Sillero Denamiel, María Remedios | es |
dc.date.accessioned | 2021-04-20T11:57:14Z | |
dc.date.available | 2021-04-20T11:57:14Z | |
dc.date.issued | 2020-03-02 | |
dc.identifier.citation | Blanquero Bravo, R., Carrizosa Priego, E.J., Ramírez Cobo, J. y Sillero Denamiel, M.R. (2020). A cost-sensitive constrained Lasso. Advances in Data Analysis and Classification, 15 (1), 121-158. | |
dc.identifier.issn | 1862-5347 | es |
dc.identifier.issn | 1862-5355 | es |
dc.identifier.uri | https://hdl.handle.net/11441/107464 | |
dc.description.abstract | The Lasso has become a benchmark data analysis procedure, and numerous variants
have been proposed in the literature. Although the Lasso formulations are stated so
that overall prediction error is optimized, no full control over the accuracy prediction
on certain individuals of interest is allowed. In this work we propose a novel version
of the Lasso in which quadratic performance constraints are added to Lasso-based
objective functions, in such a way that threshold values are set to bound the prediction
errors in the different groups of interest (not necessarily disjoint). As a result, a constrained
sparse regression model is defined by a nonlinear optimization problem. This
cost-sensitive constrained Lasso has a direct application in heterogeneous samples
where data are collected from distinct sources, as it is standard in many biomedical
contexts. Both theoretical properties and empirical studies concerning the new method
are explored in this paper. In addition, two illustrations of the method on biomedical
and sociological contexts are considered. | es |
dc.format | application/pdf | es |
dc.format.extent | 37 p. | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Advances in Data Analysis and Classification, 15 (1), 121-158. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Performance constraints | es |
dc.subject | Cost-sensitive learning | es |
dc.subject | Sparse solutions | es |
dc.subject | Sample average approximation | es |
dc.subject | Heterogeneity | es |
dc.subject | Lasso | es |
dc.title | A cost-sensitive constrained Lasso | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa | es |
dc.relation.publisherversion | https://doi.org/10.1007/s11634-020-00389-5 | es |
dc.identifier.doi | 10.1007/s11634-020-00389-5 | es |
dc.contributor.group | Universidad de Sevilla. FQM329: Optimización | es |
dc.journaltitle | Advances in Data Analysis and Classification | es |
dc.publication.volumen | 15 | es |
dc.publication.issue | 1 | es |
dc.publication.initialPage | 121 | es |
dc.publication.endPage | 158 | es |