Blog: A New System Increases the Reliability of Opinion Polls

A New System Increases the Reliability of Opinion Polls
Universidad Politecnica de Madrid (Spain) (04/26/11) Eduardo Martinez

Universidad Politecnica de Madrid researchers have developed a fuzzy neural network that uses a numerical and categorical imputation method to reconstruct incomplete data sets, which could be used to determine the voting intention of a voter that has not answered all the opinion poll questions with near 90 percent accuracy. The system, developed by Jesus Cardenosa and Pilar Rey del Castillo, also can be used for medical diagnosis or surveying using categorical variables. The system works by first defining the distances between categories using fuzzy logic. It then determines where each category is located within the different dataset spaces using the neural network. Finally, the system extends the network architecture to all the data and processes the missing data.

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