Publikation: Multi-label Classification with ART Neural Networks
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2009
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Published
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2009 Second International Workshop on Knowledge Discovery and Data Mining. IEEE, 2009, pp. 144-147. ISBN 978-0-7695-3543-2. Available under: doi: 10.1109/WKDD.2009.200
Zusammenfassung
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.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Multi-label classification, neural networks, Fuzzy ARTMAP
Konferenz
2009 Second International Workshop on Knowledge Discovery and Data Mining (WKDD), 23. Jan. 2009 - 25. Jan. 2009, Moscow, Russia
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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, 2009, pp. 144-147. ISBN 978-0-7695-3543-2. Available under: doi: 10.1109/WKDD.2009.200BibTex
@inproceedings{Sapozhnikova2009-01Multi-5912,
year={2009},
doi={10.1109/WKDD.2009.200},
title={Multi-label Classification with ART Neural Networks},
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}
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<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>
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