Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Robust Learning with Missing Data
Machine Learning
Learning Bayesian Networks
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
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
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks
Artificial Intelligence in Medicine
Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours
Computers in Biology and Medicine
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This paper introduces a novel conservative feature subset selection method with incomplete data sets. The method is conservative in the sense that it selects the minimal subset of features that renders the rest of the features independent of the target (the class variable) without making any assumption about the missing data mechanism. This is achieved in the context of determining the Markov blanket of the target that reflects the worst-case assumption about the missing data mechanism, including the case when data are not missing at random. An application of the method on synthetic and real-world incomplete data is carried out to illustrate its practical relevance. The method is compared against state-of-the-art approaches such as the expectation-maximization (EM) algorithm and the available case technique.