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Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs
Cita
Rey del Castillo, Pilar and Cardeñosa Lera, Jesús 
ORCID: https://orcid.org/0000-0002-8828-9587
(2009).
Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs.
"World Academy Of Science, Engineering And Technology", v. 55
;
pp. 628-635.
ISSN 2070-3724.
ORCID: https://orcid.org/0000-0002-8828-9587
(2009).
Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs.
"World Academy Of Science, Engineering And Technology", v. 55
;
pp. 628-635.
ISSN 2070-3724.
Descripción
| Título: | Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Artículo |
| Título de Revista/Publicación: | World Academy Of Science, Engineering And Technology |
| Fecha: | Julio 2009 |
| ISSN: | 2070-3724 |
| Volumen: | 55 |
| Materias: | |
| ODS: | |
| Escuela: | Facultad de Informática (UPM) [antigua denominación] |
| Departamento: | Inteligencia Artificial |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson’s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.
Más información
| ID de Registro: | 13626 |
|---|---|
| Identificador DC: | https://oa.upm.es/13626/ |
| Identificador OAI: | oai:oa.upm.es:13626 |
| URL Oficial: | http://www.waset.org/ |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 16 Oct 2012 08:56 |
| Ultima Modificación: | 21 Abr 2016 12:57 |
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