Multi-label Classification with ART Neural Networks


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SAPOZHNIKOVA, Elena, 2009. Multi-label Classification with ART Neural Networks. 2009 Second International Workshop on Knowledge Discovery and Data Mining (WKDD). Moscow, Russia, 23. Jan 2009 - 25. Jan 2009. In: 2009 Second International Workshop on Knowledge Discovery and Data Mining. IEEE, pp. 144-147. ISBN 978-0-7695-3543-2. Available under: doi: 10.1109/WKDD.2009.200

@inproceedings{Sapozhnikova2009-01Multi-5912, title={Multi-label Classification with ART Neural Networks}, year={2009}, doi={10.1109/WKDD.2009.200}, isbn={978-0-7695-3543-2}, publisher={IEEE}, booktitle={2009 Second International Workshop on Knowledge Discovery and Data Mining}, pages={144--147}, author={Sapozhnikova, Elena} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dcterms:bibliographicCitation>First publ. in: Proceedings / Second International Workshop on Knowledge Discovery and Data Mining (WKDD 2009), 2009, pp. 144-147</dcterms:bibliographicCitation> <dcterms:rights rdf:resource=""/> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dcterms:isPartOf rdf:resource=""/> <dc:language>eng</dc:language> <dc:contributor>Sapozhnikova, Elena</dc:contributor> <bibo:uri rdf:resource=""/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Sapozhnikova, Elena</dc:creator> <dc:date rdf:datatype="">2011-03-24T16:01:21Z</dc:date> <dcterms:hasPart rdf:resource=""/> <dc:rights>terms-of-use</dc:rights> <dc:format>application/pdf</dc:format> <dspace:hasBitstream rdf:resource=""/> <dcterms:title>Multi-label Classification with ART Neural Networks</dcterms:title> <dcterms:issued>2009-01</dcterms:issued> <dspace:isPartOfCollection rdf:resource=""/> <dcterms:available rdf:datatype="">2011-03-24T16:01:21Z</dcterms:available> <dcterms:abstract xml:lang="eng">Multi-label Classification (MC) is a classification task with instances labelled by multiple classes rather than just one. This task becomes increasingly important in such fields as gene function prediction or web-mining. Early approaches to MC were based on learning independent binary classifiers for each class and combining their outputs in order to obtain multi-label predictions. Alternatively, a classifier can be directly trained to predict a label set of an unknown size for each unseen instance. Recently, several direct multi-label learning algorithms have been proposed. This paper investigates a novel method to solve a MC task by using an Adaptive Resonance Theory (ART) neural network. A modified Fuzzy ARTMAP algorithm Multi-Label-FAM (ML-FAM) was applied to classification of multi-label data. The obtained preliminary results on the Yeast data set and their comparison with the results of existing algorithms demonstrate the effectiveness of the proposed approach.</dcterms:abstract> </rdf:Description> </rdf:RDF>

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