Optimizing Logistic Regression Coefficients for Discrimination and Calibration Using Estimation of Distribution Algorithms.

Robles Forcada, Víctor and Bielza Lozoya, Maria Concepcion and Larrañaga Múgica, Pedro and González Tortosa, Santiago and Ohno-Machado, Lucila (2008). Optimizing Logistic Regression Coefficients for Discrimination and Calibration Using Estimation of Distribution Algorithms.. "Top-Madrid-", v. 16 (n. 2); pp. 345-366. ISSN 1134-5764. https://doi.org/10.1007/s11750-008-0054-3.

Description

Title: Optimizing Logistic Regression Coefficients for Discrimination and Calibration Using Estimation of Distribution Algorithms.
Author/s:
  • Robles Forcada, Víctor
  • Bielza Lozoya, Maria Concepcion
  • Larrañaga Múgica, Pedro
  • González Tortosa, Santiago
  • Ohno-Machado, Lucila
Item Type: Article
Título de Revista/Publicación: Top-Madrid-
Date: December 2008
ISSN: 1134-5764
Volume: 16
Subjects:
Freetext Keywords: Logistic regression, evolutionary algorithms, estimation of distribution algorithms, calibration and discrimination.
Faculty: Facultad de Informática (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (757kB) | Preview

Abstract

Logistic regression is a simple and efficient supervised learning algorithm for estimating the probability of an outcome or class variable. In spite of its simplicity, logistic regression has shown very good performance in a range of fields. It is widely accepted in a range of fields because its results are easy to interpret. Fitting the logistic regression model usually involves using the principle of maximum likelihood. The Newton–Raphson algorithm is the most common numerical approach for obtaining the coefficients maximizing the likelihood of the data. This work presents a novel approach for fitting the logistic regression model based on estimation of distribution algorithms (EDAs), a tool for evolutionary computation. EDAs are suitable not only for maximizing the likelihood, but also for maximizing the area under the receiver operating characteristic curve (AUC). Thus, we tackle the logistic regression problem from a double perspective: likelihood-based to calibrate the model and AUC-based to discriminate between the different classes. Under these two objectives of calibration and discrimination, the Pareto front can be obtained in our EDA framework. These fronts are compared with those yielded by a multiobjective EDA recently introduced in the literature.

More information

Item ID: 2392
DC Identifier: http://oa.upm.es/2392/
OAI Identifier: oai:oa.upm.es:2392
DOI: 10.1007/s11750-008-0054-3
Official URL: http://www.springer.com/business+%26+management/operations+research/journal/11750
Deposited by: Memoria Investigacion
Deposited on: 25 Feb 2010 09:50
Last Modified: 20 Apr 2016 12:06
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM