relation: https://khub.utp.edu.my/scholars/10964/
title: Fuzzy ARTMAP with binary relevance for multi-label classification
creator: Yuan, L.X.
creator: Tan, S.C.
creator: Goh, P.Y.
creator: Lim, C.P.
creator: Watada, J.
description: 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.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2018
type: Article
type: PeerReviewed
identifier:   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.  ISSN 21903018     
relation: 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
relation: 10.1007/978-3-319-59424-8₁₂
identifier: 10.1007/978-3-319-59424-8₁₂