Regularized logistic regression without a penalty term: an application to cancer classification with microarray data

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.

Description

Title: Regularized logistic regression without a penalty term: an application to cancer classification with microarray data
Author/s:
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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Abstract

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.

Funding Projects

Type
Code
Acronym
Leader
Title
Government of Spain
TIN2007-62626
Unspecified
Unspecified
Unspecified
Government of Spain
TIN2007-67148
Unspecified
Unspecified
Unspecified
Government of Spain
TIN2005-03824
Unspecified
Unspecified
Unspecified
Government of Spain
2010- CSD2007-00018
Unspecified
Unspecified
Unspecified

More information

Item ID: 72871
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
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