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


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.

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Correspondence to Pilar Rey-del-Castillo.

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Rey-del-Castillo, P., Cardeñosa, J. Fuzzy min–max neural networks for categorical data: application to missing data imputation. Neural Comput & Applic 21, 1349–1362 (2012).

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  • Classification
  • Fuzzy systems
  • Fuzzy min–max neural networks
  • Imputation
  • Missing data