Multivariate Imputation of Qualitative Missing Data Using Bayesian Networks

In this paper we propose a methodology for the imputation of qualitative missing data using Bayesian networks. The idea is to learn a Bayesian network from the available complete data and use it to simultaneously impute all the missing cells in a register by means of abductive inference. The proposed methodology is experimentally tested and compared with the use of classification trees.

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