Learning Classifiers from Distributed, Ontology-Extended Data Sources

dc.contributor.author Caragea, Doina
dc.contributor.author Zhang, Jun
dc.contributor.author Pathak, Jyotishman
dc.contributor.author Honavar, Vasant
dc.contributor.department Computer Science
dc.date 2018-02-13T23:24:54.000
dc.date.accessioned 2020-06-30T01:55:40Z
dc.date.available 2020-06-30T01:55:40Z
dc.date.issued 2005-01-01
dc.description.abstract <p>There is an urgent need for sound approaches to integrative and collaborative analysis of large, autonomous (and hence, inevitably semantically heterogeneous) data sources in several increasingly data-rich application domains. In this paper, we precisely formulate and solve the problem of learning classifiers from such data sources, in a setting where each data source has a hierarchical ontology associated with it and semantic correspondences between data source ontologies and a user ontology are supplied. given user-supplied semantic correspondences between data source ontologies and the user ontology. The proposed approach yields algorithms for learning a broad class of classifiers (including Bayesian networks, decision trees, etc.) from semantically heterogeneous distributed data with strong performance guarantees relative to their centralized counterparts. We illustrate the application of the proposed approach in the case of learning Naive Bayes classifiers from distributed, ontology-extended data sources.</p>
dc.identifier archive/lib.dr.iastate.edu/cs_techreports/189/
dc.identifier.articleid 1214
dc.identifier.contextkey 5438131
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath cs_techreports/189
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/20004
dc.source.bitstream archive/lib.dr.iastate.edu/cs_techreports/189/caragea_TR_sem.pdf|||Fri Jan 14 21:47:26 UTC 2022
dc.subject.disciplines Artificial Intelligence and Robotics
dc.subject.keywords Machine learning
dc.subject.keywords knowledge discovery
dc.subject.keywords semantically heterogeneous data
dc.subject.keywords ontologies
dc.subject.keywords attribute value taxonomies
dc.subject.keywords naive Bayes algorithm
dc.title Learning Classifiers from Distributed, Ontology-Extended Data Sources
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication f7be4eb9-d1d0-4081-859b-b15cee251456
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