Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs
Data(s) |
01/07/2009
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Resumo |
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. |
Formato |
application/pdf |
Identificador | |
Idioma(s) |
eng |
Publicador |
Facultad de Informática (UPM) |
Relação |
http://oa.upm.es/13626/1/INVE_MEM_2009_98864.pdf http://www.waset.org/ |
Direitos |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess |
Fonte |
World Academy Of Science, Engineering And Technology, ISSN 2070-3724, 2009-07, Vol. 55 |
Palavras-Chave | #Matemáticas #Informática |
Tipo |
info:eu-repo/semantics/article Artículo PeerReviewed |