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...

Full description

Main Authors: Yuan, L.X., Tan, S.C., Goh, P.Y., Lim, C.P., Watada, J.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.22023 /
Published: Springer Science and Business Media Deutschland GmbH 2018
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/22023/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-eprints.22023
recordtype eprints
spelling utp-eprints.220232019-02-20T01:57:21Z Fuzzy ARTMAP with binary relevance for multi-label classification Yuan, L.X. Tan, S.C. Goh, P.Y. Lim, C.P. Watada, J. 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. Springer Science and Business Media Deutschland GmbH 2018 Article PeerReviewed 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 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. http://eprints.utp.edu.my/22023/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
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.
format Article
author Yuan, L.X.
Tan, S.C.
Goh, P.Y.
Lim, C.P.
Watada, J.
spellingShingle Yuan, L.X.
Tan, S.C.
Goh, P.Y.
Lim, C.P.
Watada, J.
Fuzzy ARTMAP with binary relevance for multi-label classification
author_sort Yuan, L.X.
title Fuzzy ARTMAP with binary relevance for multi-label classification
title_short Fuzzy ARTMAP with binary relevance for multi-label classification
title_full Fuzzy ARTMAP with binary relevance for multi-label classification
title_fullStr Fuzzy ARTMAP with binary relevance for multi-label classification
title_full_unstemmed Fuzzy ARTMAP with binary relevance for multi-label classification
title_sort fuzzy artmap with binary relevance for multi-label classification
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2018
url 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/22023/
_version_ 1741196559403450368
score 11.62408

Similar Items