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Bielza Lozoya, María Concepción ORCID: https://orcid.org/0000-0001-7109-2668, Robles Forcada, Víctor
ORCID: https://orcid.org/0000-0003-3937-2269 and Larrañaga Múgica, Pedro María
ORCID: https://orcid.org/0000-0002-1885-4501
(2011).
Regularized logistic regression without a penalty term: an application to cancer classification with microarray data.
"Expert Systems with Applications", v. 38
(n. 5);
pp. 5110-5118.
ISSN 0957-4174.
https://doi.org/10.1016/j.eswa.2010.09.140.
Title: | Regularized logistic regression without a penalty term: an application to cancer classification with microarray data |
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Author/s: |
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Item Type: | Article |
Título de Revista/Publicación: | Expert Systems with Applications |
Date: | May 2011 |
ISSN: | 0957-4174 |
Volume: | 38 |
Subjects: | |
Freetext Keywords: | Logistic regression, Regularization, Estimation of distribution algorithms, Cancer classification, Microarray data |
Faculty: | Facultad de Informática (UPM) |
Department: | Inteligencia Artificial |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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Regularized logistic regression is a useful classification method for problems with few samples and a huge number of variables. This regression needs to determine the regularization term, which amounts to searching for the optimal penalty parameter and the norm of the regression coefficient vector. This paper presents a new regularized logistic regression method based on the evolution of the regression coefficients using estimation of distribution algorithms. The main novelty is that it avoids the determination of the regularization term. The chosen simulation method of new coefficients at each step of the evolutionary process guarantees their shrinkage as an intrinsic regularization. Experimental results comparing the behavior of the proposed method with Lasso and ridge logistic regression in three cancer classification problems with microarray data are shown.
Item ID: | 72871 |
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DC Identifier: | https://oa.upm.es/72871/ |
OAI Identifier: | oai:oa.upm.es:72871 |
DOI: | 10.1016/j.eswa.2010.09.140 |
Official URL: | https://www.sciencedirect.com/science/article/pii/... |
Deposited by: | Biblioteca Facultad de Informatica |
Deposited on: | 21 Mar 2023 09:24 |
Last Modified: | 21 Mar 2023 09:24 |