Memorias de investigación
Research Publications in journals:
Optimizing Logistic Regression Coefficients for Discrimination and Calibration Using Estimation of Distribution Algorithms
Year:2008
Research Areas
  • Operative research,
  • Statistics
Information
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.
International
Si
JCR
Si
Title
TOP
ISBN
1134-5764
Impact factor JCR
0,694
Impact info
Volume
10.1007/s11750-008-0054-3
Journal number
0
From page
345
To page
366
Month
ENERO
Ranking
Participants
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de análisis de decisiones y estadística
  • Departamento: Inteligencia Artificial
  • Departamento: Arquitectura y Tecnología de Sistemas Informáticos
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Grupo de Investigación: Data Mining Engineering (DaME) Ingeniería de Minería de datos