Yuan, L.X. and Tan, S.C. and Goh, P.Y. and Lim, C.P. and Watada, J. (2018) Fuzzy ARTMAP with binary relevance for multi-label classification. Smart Innovation, Systems and Technologies, 73. pp. 127-135.
Full text not available from this repository.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. © Springer International Publishing AG 2018.
Item Type: | Article |
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Impact Factor: | cited By 0; Conference of 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017 ; Conference Date: 21 June 2017 Through 23 June 2017; Conference Code:192309 |
Uncontrolled Keywords: | Bins; Data handling, Adaptive resonance theory neural networks; Binary relevances; Data classification; Data classification problems; Fuzzy ARTMAP; Multi label classification; Multi-label, Classification (of information) |
Departments / MOR / COE: | Research Institutes > Institute for Autonomous Systems |
Depositing User: | Mr Ahmad Suhairi Mohamed Lazim |
Date Deposited: | 01 Aug 2018 01:01 |
Last Modified: | 20 Feb 2019 01:57 |
URI: | http://scholars.utp.edu.my/id/eprint/22023 |