Hierarchical Bayesian Networks: An Approach to Classification and Learning for Structured Data.

E Gyftodimos, PA Flach

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

21 Citations (Scopus)

Abstract

Bayesian Networks are one of the most popular formalisms for reasoning under uncertainty. Hierarchical Bayesian Networks (HBNs) are an extension of Bayesian Networks that are able to deal with structured domains, using knowledge about the structure of the data to introduce a bias that can contribute to improving inference and learning methods. In effect, nodes in an HBN are (possibly nested) aggregations of simpler nodes. Every aggregate node is itself an HBN modelling independences inside a subset of the whole world under consideration. In this paper we discuss how HBNs can be used as Bayesian classifiers for structured domains. We also discuss how HBNs can be further extended to model more complex data structures, such as lists or sets, and we present the results of preliminary experiments on the mutagenesis dataset.
Translated title of the contributionHierarchical Bayesian Networks: An Approach to Classification and Learning for Structured Data.
Original languageEnglish
Title of host publicationUnknown
EditorsGeorge A. Vouros, Themis Panayiotopoulos
PublisherSpringer
Pages291 - 300
Number of pages9
ISBN (Print)3540219374
Publication statusPublished - May 2004

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of Methods and Applications of Artificial Intelligence, Third Hellenic Conference on AI (SETN 2004)

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