A novel feature selection methodology for automated inspection systems.
IEEE Trans Pattern Anal Mach Intell
; 31(7): 1338-44, 2009 Jul.
Article
em En
| MEDLINE
| ID: mdl-19443930
ABSTRACT
This paper proposes a new feature selection methodology. The methodology is based on the stepwise variable selection procedure, but, instead of using the traditional discriminant metrics such as Wilks' Lambda, it uses an estimation of the misclassification error as the figure of merit to evaluate the introduction of new features. The expected misclassification error rate (MER) is obtained by using the densities of a constructed function of random variables, which is the stochastic representation of the conditional distribution of the quadratic discriminant function estimate. The application of the proposed methodology results in significant savings of computational time in the estimation of classification error over the traditional simulation and cross-validation methods. One of the main advantages of the proposed method is that it provides a direct estimation of the expected misclassification error at the time of feature selection, which provides an immediate assessment of the benefits of introducing an additional feature into an inspection/classification algorithm.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Análise de Falha de Equipamento
/
Modelos Teóricos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
IEEE Trans Pattern Anal Mach Intell
Ano de publicação:
2009
Tipo de documento:
Article