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

Robles Forcada, Víctor; Bielza Lozoya, Maria Concepcion; Larrañaga Múgica, Pedro; González Tortosa, Santiago y 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.

Descripción

Título: Optimizing Logistic Regression Coefficients for Discrimination and Calibration Using Estimation of Distribution Algorithms.
Autor/es:
  • Robles Forcada, Víctor
  • Bielza Lozoya, Maria Concepcion
  • Larrañaga Múgica, Pedro
  • González Tortosa, Santiago
  • Ohno-Machado, Lucila
Tipo de Documento: Artículo
Título de Revista/Publicación: Top-Madrid-
Fecha: Diciembre 2008
Volumen: 16
Materias:
Palabras Clave Informales: Logistic regression, evolutionary algorithms, estimation of distribution algorithms, calibration and discrimination.
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 2392
Identificador DC: http://oa.upm.es/2392/
Identificador OAI: oai:oa.upm.es:2392
Identificador DOI: 10.1007/s11750-008-0054-3
URL Oficial: http://www.springer.com/business+%26+management/operations+research/journal/11750
Depositado por: Memoria Investigacion
Depositado el: 25 Feb 2010 09:50
Ultima Modificación: 20 Abr 2016 12:06
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