Using Information Gain in Data Fusion and Ranking

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dc.AffiliationOctober University for modern sciences and Arts (MSA)
dc.contributor.authorM Hafez, Mohamed
dc.contributor.authorH El-Bastawissy, Ali
dc.contributor.authorM Hegazy, Osman
dc.date.accessioned2020-01-30T08:10:42Z
dc.date.available2020-01-30T08:10:42Z
dc.date.issued2014
dc.descriptionMSA Google Scholaren_US
dc.description.abstractEntropy and information gain have been traditionally used to measure association between inputs and outputs. In this paper, Information gain is used to measure and decide the level of dependency or relevance between attributes. A data fusion technique based on information gain measures in a virtual data integration environment is introduced. After the detection and clustering of duplicates, the fused records are ranked and provided to the user in the final answer set with a preference score associated with each answeren_US
dc.description.sponsorshipRecent Advances in Computer Engineering, Communications and Information Technologyen_US
dc.identifier.isbn978-960-474-361-2
dc.identifier.urihttps://cutt.ly/yrYsAXz
dc.language.isoenen_US
dc.publisherRecent Advances in Computer Engineering, Communications and Information Technologyen_US
dc.relation.ispartofseriesRecent Advances in Computer Engineering, Communications and Information Technology;
dc.subjectOctober University for University for Data integrationen_US
dc.subjectdata fusionen_US
dc.subjectInformation gainen_US
dc.subjectduplicates detectoren_US
dc.subjectconflict resolutionen_US
dc.subjectranking answersen_US
dc.titleUsing Information Gain in Data Fusion and Rankingen_US
dc.typeBook chapteren_US

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