%0 Journal Article %A Pilar Rey-del-Castillo and Jesús Cardeñosa %D 2009 %J International Journal of Computer and Information Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 31, 2009 %T Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs %U https://publications.waset.org/pdf/7285 %V 31 %X 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. %P 1843 - 1850