%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