Fuzzy ARTMAP with binary relevance for multi-label classification
In 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 bench...
| Main Authors: | Yuan, L.X., Tan, S.C., Goh, P.Y., Lim, C.P., Watada, J. |
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| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.21373 / |
| Published: |
Springer Science and Business Media Deutschland GmbH
2018
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020416745&doi=10.1007%2f978-3-319-59424-8_12&partnerID=40&md5=ad2af4eaccfb6c1c8fc243100d211971 http://eprints.utp.edu.my/21373/ |
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| Summary: |
In 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. © Springer International Publishing AG 2018. |
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