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Memorias de investigación
Artículos en revistas:
An L1-regularized naive Bayes-inspired classifier for discarding redundant predictors
Año:2013
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
The naïve Bayes model is a simple but often satisfactory supervised classification method.The original naïve Bayes scheme, does, however, have a serious weakness, namely, the harmful effect of redundant predictors. In this paper, we stu dy how to apply a regularization technique to learn a computationally efficient classifier that is inspired by naïve Bayes. The proposed formulation, combined with an L1-penalty, is capable of discarding harmful, redundant predictors. A modification of the LARS algorithm is devised to solve this problem. We tackle both real-valued and discrete predictors, assuring that our method is applicable to a wide range of data. In the experimental section, we empirically study the effect of redundant and irrelevant predictors. We also test the method on a high-dimensional data set from the neuroscience field, where there are many more predictors than data cases. Finally, we run the method on a real data set than combines categorical with numeric predictors. Our approach is compared with several naïve Bayes variants and other classification algorithms (SVM and kNN), and is shown to be competitive.
Internacional
Si
JCR del ISI
Si
Título de la revista
International Journal on Artificial Intelligence Tools
ISSN
0218-2130
Factor de impacto JCR
0,25
Información de impacto
Volumen
22
DOI
Número de revista
4
Desde la página
1350019
Hasta la página
Mes
SIN MES
Ranking
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Participantes
  • Autor: D. Vidaurre
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: P. Larrañaga
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
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