Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs

Rey del Castillo, Pilar and Cardeñosa Lera, Jesús (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.

Description

Title: Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs
Author/s:
  • Rey del Castillo, Pilar
  • Cardeñosa Lera, Jesús
Item Type: Article
Título de Revista/Publicación: World Academy Of Science, Engineering And Technology
Date: July 2009
ISSN: 2070-3724
Volume: 55
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 13626
DC Identifier: http://oa.upm.es/13626/
OAI Identifier: oai:oa.upm.es:13626
Official URL: http://www.waset.org/
Deposited by: Memoria Investigacion
Deposited on: 16 Oct 2012 08:56
Last Modified: 21 Apr 2016 12:57
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