Hierarchical Bayesian Networks: An Approach to Classification and Learning for Structured Data
作者： Elias Gyftodimos , Peter A. Flach
DOI: 10.1007/978-3-540-24674-9_31
关键词: Bayesian probability 、 Artificial intelligence 、 Bayesian network 、 Conditional probability table 、 Machine learning 、 Node (computer science) 、 Computer science 、 Data modeling 、 Data mining 、 Inference
摘要: Bayesian Networks are one of the most popular formalisms for reasoning under uncertainty. Hierarchical (HBNs) an extension that able to deal with structured domains, using knowledge about structure data introduce a bias can contribute improving inference and learning methods. In effect, nodes in HBN (possibly nested) aggregations simpler nodes. Every aggregate node is itself modelling independences inside subset whole world consideration. this paper we discuss how HBNs be used as classifiers domains. We also further extended model more complex structures, such lists or sets, present results preliminary experiments on mutagenesis dataset.
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