Sandra Gabaglio

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We present a novel approach for multilabel classification based on an ensemble of Bayesian networks. The class variables are connected by a tree; each model of the ensemble uses a different class as root of the tree. We assume the features to be conditionally independent given the classes, thus generalizing the naive Bayes assumption to the multiclass case.(More)
It has been a long time since the scientific community started to speculate upon the presence of Helicobacter pylori (HP) in periodontal pockets as an extra-gastric reservoir responsible for gastric relapses after eradication therapy. The aim of this study is to evaluate the presence of oral HP in a group of patients who underwent examination for gastric(More)
In previous work, we devised an approach for multilabel classification based on an ensemble of Bayesian networks. It was characterized by an efficient structural learning and by high accuracy. Its shortcoming was the high computational complexity of the MAP inference, necessary to identify the most probable joint configuration of all classes. In this work,(More)
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