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Learning different concept hierarchies and the relations between them from classified data

Learning different concept hierarchies and the relations between them from classified data

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BENITES DE AZEVEDO E SOUZA, Fernando, Elena SAPOZHNIKOVA, 2012. Learning different concept hierarchies and the relations between them from classified data. In: MAGDALENA-BENEDITO, Rafael, ed. and others. Intelligent data analysis for real-life applications : theory and practice. Hershey, PA:Information Science Reference, pp. 18-34. ISBN 978-1-4666-1806-0

@incollection{Benites de Azevedo e Souza2012Learn-21455, title={Learning different concept hierarchies and the relations between them from classified data}, year={2012}, isbn={978-1-4666-1806-0}, address={Hershey, PA}, publisher={Information Science Reference}, booktitle={Intelligent data analysis for real-life applications : theory and practice}, pages={18--34}, editor={Magdalena-Benedito, Rafael}, author={Benites de Azevedo e Souza, Fernando and Sapozhnikova, Elena} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/21455"> <dc:creator>Benites de Azevedo e Souza, Fernando</dc:creator> <dc:language>eng</dc:language> <dcterms:title>Learning different concept hierarchies and the relations between them from classified data</dcterms:title> <dcterms:abstract xml:lang="eng">The objective of this chapter is to show how the extraction of concept hierarchies and finding relations between them can be effectively coupled with a multi-label classification task. We introduce a data mining system which performs classification and addresses both issues by means of association rule mining. The proposed system has been tested on two real-world datasets with the class labels of each dataset coming from two different class hierarchies. Several experiments on hierarchy extraction and concept relation were conducted in order to evaluate the system and three different interestingness measures were applied, to select the most important relations between concepts. One of the measures was developed by the authors. The experimental results showed that the system is able to infer quite accurate concept hierarchies and associations among the concepts. It is therefore well suited for classification-based reasoning.</dcterms:abstract> <dcterms:issued>2012</dcterms:issued> <dc:contributor>Benites de Azevedo e Souza, Fernando</dc:contributor> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/21455"/> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dc:contributor>Sapozhnikova, Elena</dc:contributor> <dc:creator>Sapozhnikova, Elena</dc:creator> <dcterms:bibliographicCitation>Intelligent data analysis for real-life applications : theory and practice / Rafael Magdalena-Benedito ... - Hershey, PA : Information Science Reference, 2012. - S. 18-34. - ISBN 978-1-4666-1806-0</dcterms:bibliographicCitation> <dc:rights>deposit-license</dc:rights> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-02-08T08:43:58Z</dcterms:available> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-02-08T08:43:58Z</dc:date> </rdf:Description> </rdf:RDF>

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