2015 | OriginalPaper | Buchkapitel
Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data
verfasst von : Konstantinos Sechidis, Gavin Brown
Erschienen in: Machine Learning and Knowledge Discovery in Databases
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The importance of Markov blanket discovery algorithms is twofold: as the main building block in constraint-based structure learning of Bayesian network algorithms and as a technique to derive the optimal set of features in filter feature selection approaches. Equally, learning from partially labelled data is a crucial and demanding area of machine learning, and extending techniques from fully to partially supervised scenarios is a challenging problem. While there are many different algorithms to derive the Markov blanket of fully supervised nodes, the partially-labelled problem is far more challenging, and there is a lack of principled approaches in the literature. Our work derives a generalization of the conditional tests of independence for partially labelled binary target variables, which can handle the two main partially labelled scenarios:
positive-unlabelled
and
semi-supervised
. The result is a significantly deeper understanding of how to control false negative errors in Markov Blanket discovery procedures and how unlabelled data can help.