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Memorias de investigación
Artículos en revistas:
Forward Stagewise Naive Bayes
Año:2012
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
The naïve Bayes approach is a simple but often satisfactory method for supervised classification. In this paper, we focus on the naïve Bayes model and propose the application of regularization techniques to learn a naïve Bayes classifier. The main contribution of the paper is a stagewise version of the selective naïve Bayes, which can be considered a regularized version of the naïve Bayes model. We call it forward stagewise naïve Bayes. For comparison?s sake, we also introduce an explicitly regularized formulation of the naïve Bayes model, where conditional independence (absence of arcs) is promoted via an L1/L2-group penalty on the parameters that define the conditional probability distributions. Although already published in the literature, this idea has only been applied for continuous predictors. We extend this formulation to discrete predictors and propose a modification that yields an adaptive penalization. We show that, whereas the L1/L2 group penalty formulation only discards irrelevant predictors, the forward stagewise naïve Bayes can discard both irrelevant and redundant predictors, which are known to be harmful for the naïve Bayes classifier. Both approaches, however, usually improve the classical naïve Bayes model?s accuracy.
Internacional
Si
JCR del ISI
No
Título de la revista
Progress in Artificial Intelligence
ISSN
2192-6352
Factor de impacto JCR
Información de impacto
Volumen
1
DOI
Número de revista
Desde la página
57
Hasta la página
69
Mes
SIN MES
Ranking
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Participantes
  • Autor: Diego Vidaurre Henche (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial
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