ART-based Neural Networks for Multi-Label Classification

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SAPOZHNIKOVA, Elena, 2009. ART-based Neural Networks for Multi-Label Classification. In: ADAMS, Niall M., ed., Céline ROBARDET, ed., Arno SIEBES, ed., Jean-François BOULICAUT, ed.. Advances in Intelligent Data Analysis VIII. Berlin, Heidelberg:Springer Berlin Heidelberg, pp. 167-177. ISBN 978-3-642-03914-0. Available under: doi: 10.1007/978-3-642-03915-7_15

@inproceedings{Sapozhnikova2009ARTba-2994, title={ART-based Neural Networks for Multi-Label Classification}, year={2009}, doi={10.1007/978-3-642-03915-7_15}, number={5772}, isbn={978-3-642-03914-0}, address={Berlin, Heidelberg}, publisher={Springer Berlin Heidelberg}, series={Lecture Notes in Computer Science}, booktitle={Advances in Intelligent Data Analysis VIII}, pages={167--177}, editor={Adams, Niall M. and Robardet, Céline and Siebes, Arno and Boulicaut, Jean-François}, 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:rights rdf:resource=""/> <dcterms:isPartOf rdf:resource=""/> <dcterms:available rdf:datatype="">2011-03-23T10:15:40Z</dcterms:available> <dspace:isPartOfCollection rdf:resource=""/> <dcterms:title>ART-based Neural Networks for Multi-Label Classification</dcterms:title> <dc:date rdf:datatype="">2011-03-23T10:15:40Z</dc:date> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dcterms:abstract xml:lang="eng">Multi-label classification is an active and rapidly developing research area of data analysis. It becomes increasingly important in such fields as gene function prediction, text classification or web mining. This task corresponds to classification of instances labeled by multiple classes rather than just one. Traditionally, it was solved by learning independent binary classifiers for each class and combining their outputs 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 machine learning algorithms have been proposed. This paper presents a novel approach based on ART (Adaptive Resonance Theory) neural networks. The Fuzzy ARTMAP and ARAM algorithms were modified in order to improve their multi-label classification performance and were evaluated on benchmark datasets. Comparison of experimental results with the results of other multi-label classifiers shows the effectiveness of the proposed approach.</dcterms:abstract> <dc:rights>terms-of-use</dc:rights> <bibo:uri rdf:resource=""/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:language>eng</dc:language> <dcterms:issued>2009</dcterms:issued> <dc:contributor>Sapozhnikova, Elena</dc:contributor> <dc:creator>Sapozhnikova, Elena</dc:creator> <dcterms:bibliographicCitation>First publ. in: Advances in Intelligent Data Analysis VIII : 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009 / Niall M. Adams ... (eds.). (= LNCS ; 5772). Berlin : Springer, 2009, pp. 167-177</dcterms:bibliographicCitation> </rdf:Description> </rdf:RDF>

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