Generation of Incompliete Test-Data usinng Bayesinan Networks

Abstract

We introduce a new method based on Bayesian Network formalism for automatically generating incomplete datasets. This method can either be configured randomly to generate various datasets with respect to a global percentage of missing data or manually in order to handle many parameters. [1] proposed three types of missing data: MCAR (missing completly at… (More)
DOI: 10.1109/IJCNN.2007.4371332

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