@article{(Open Science Index):https://publications.waset.org/pdf/7285, title = {Categorical Missing Data Imputation Using Fuzzy Neural Networks with Numerical and Categorical Inputs}, author = {Pilar Rey-del-Castillo and Jesús Cardeñosa}, country = {}, institution = {}, abstract = {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. }, journal = {International Journal of Computer and Information Engineering}, volume = {3}, number = {7}, year = {2009}, pages = {1843 - 1850}, ee = {https://publications.waset.org/pdf/7285}, url = {https://publications.waset.org/vol/31}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 31, 2009}, }