Memorias de investigación
Artículos en revistas:
Fuzzy min-max neural networks for categorical data: application to missing data imputation
Año:2011
Áreas de investigación
  • Estadística,
  • Ciencias de la computación y tecnología informática,
  • Humanidades y ciencias sociales
Datos
Descripción
The fuzzy min-max neural network classifier is a supervised learning method. This classifier takes a hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min-max neural network input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The microdata ?the set of the respondents? individual answers to the questions? of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes.
Internacional
Si
JCR del ISI
Si
Título de la revista
Neural computing & applications
ISSN
0941-0643
Factor de impacto JCR
0
Información de impacto
Volumen
DOI
10.1007/s00521-011-0574-x
Número de revista
27
Desde la página
1
Hasta la página
14
Mes
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Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Validación y Aplicaciones Industriales
  • Departamento: Inteligencia Artificial