Fuzzy min-max neural networks for categorical data: application to missing data imputation

Rey del Castillo, Pilar and Cardeñosa Lera, Jesús (2012). Fuzzy min-max neural networks for categorical data: application to missing data imputation. "Neural computing & applications", v. 21 (n. 6); pp. 1349-1362. ISSN 0941-0643. https://doi.org/10.1007/s00521-011-0574-x.

Description

Title: Fuzzy min-max neural networks for categorical data: application to missing data imputation
Author/s:
  • Rey del Castillo, Pilar
  • Cardeñosa Lera, Jesús
Item Type: Article
Título de Revista/Publicación: Neural computing & applications
Date: September 2012
ISSN: 0941-0643
Volume: 21
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the 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 micro data—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.

More information

Item ID: 11867
DC Identifier: http://oa.upm.es/11867/
OAI Identifier: oai:oa.upm.es:11867
DOI: 10.1007/s00521-011-0574-x
Official URL: http://link.springer.com/article/10.1007/s00521-011-0574-x?null
Deposited by: Memoria Investigacion
Deposited on: 27 Sep 2012 09:24
Last Modified: 21 Apr 2016 11:03
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