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Fuzzy ARTMAP with binary relevance for multi-label classification
conference contribution
posted on 2018-01-01, 00:00 authored by L X Yuan, S C Tan, P Y Goh, Chee Peng LimChee Peng Lim, J WatadaIn this paper, we propose a modified supervised adaptive resonance theory neural network, namely Fuzzy ARTMAP (FAM), to undertake multi-label data classification tasks. FAM is integrated with the binary relevance (BR) technique to form BR-FAM. The effectiveness of BR-FAM is evaluated using two benchmark multi-label data classification problems. Its results are compared with those other methods in the literature. The performance of BR-FAM is encouraging, which indicate the potential of FAM-based models for handling multi-label data classification tasks.
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Event
Intelligent Decision Technologies. Conference (9th : 2017 : Algarve, Portugal)Volume
73Series
Intelligent Decision Technologies ConferencePagination
127 - 135Publisher
SpringerLocation
Algarve, PortugalPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2017-06-21End date
2017-06-23ISSN
2190-3018eISSN
2190-3026ISBN-13
9783319594231Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, Springer International Publishing AGEditor/Contributor(s)
Ireneusz Czarnowski, Robert Howlett, Lakhmi JainTitle of proceedings
KES-IDT 2017 : Smart innovation, systems and technologies : Proceedings of the 9th International KES Conference On Intelligent Decision Technologies 2017Usage metrics
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