Fuzzy ARTMAP with binary relevance for multi-label classification

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.

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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. © Springer International Publishing AG 2018.

Item Type: Article
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

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