Citation

Yuan, Lik Xun and Tan, Shing Chiang and Goh, Pey Yun and Lim, Chee Peng and Watada, Junzo (2017) Fuzzy ARTMAP with Binary Relevance for Multi-label Classification. In: International Conference on Intelligent Decision Technologies.

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Neural network,fuzzy ARTMAP ,binary relevance, multi-label classification
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	27 Mar 2021 17:08
Last Modified:	27 Mar 2021 17:08
URII:	http://shdl.mmu.edu.my/id/eprint/7540

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