@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},
	}