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

Authors: Pilar Rey-del-Castillo, Jesús Cardeñosa


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.

Keywords: Fuzzy Systems, classifier, imputation techniques, fuzzy min-max neural networks

Digital Object Identifier (DOI):

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