Statistical analysis with missing data
Statistical analysis with missing data
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Probabilistic reasoning in intelligent systems: networks of plausible inference
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ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
Learning Bayesian networks from incomplete data with stochastic search algorithms
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning probabilistic Description logic concepts: under different Assumptions on missing knowledge
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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This paper proposes a framework built on the use of Bayesian networks (BN) for representing statistical dependencies between the existing random variables and additional dummy boolean variables, which represent the presence/absence of the respective random variable value. We show how augmenting the BN with these additional variables helps pinpoint the mechanism through which missing data contributes to the classification task. The missing data mechanism is thus explicitly taken into account to predict the class variable using the data at hand. Extensive experiments on synthetic and real-world incomplete data sets reveals that the missingness information improves classification accuracy.