Multi-label Classification Based on Adaptive Resonance Theory

Naoki Masuyama1; Yusuke Nojima1; Chu Kiong Loo2; Hisao Ishibuchi3

10.1109/SSCI47803.2020.9308356

2020

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

978-1-7281-2548-0

2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)

1913-1920

1-4 Dec. 2020

Canberra, ACT, Australia

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

This paper proposes a multi-label classification algorithm based on an algorithm adaptation approach by applying the Adaptive Resonance Theory (ART) and the Bayesian approach for a label association process. In the proposed algorithm, the prior probability and likelihood are updated sequentially. Moreover, an ART-based clustering algorithm continually extracts useful information for multi-label classification, and holds the extracted information on prototype nodes generated by the clustering algorithm. Thanks to the above properties, the proposed algorithm can continually learn multi-label data. Our experimental results in this paper show that the proposed algorithm has better classification performance compared to typical multi-label classification algorithms.

multi-label classification algorithm adaptation approach adaptive resonance theory clustering

通讯

英语

CPCI-S ; EI

Frontier Research Grant) from University of Malaya[FG00317AFR] ; ONRG from Office of Naval and Research Global, UK[(ONRGNICOP-N62909-18-1-2086)/IF017-2018] ; International Collaboration Fund for project Developmental Cognitive Robot with Continual Lifelong Learning from MESTECC, Malaysia[IF0318M1006] ; National Natural Science Foundation of China[61876075] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Shenzhen Peacock Plan[KQTD2016112514355531] ; Science and Technology Innovation Committee Foundation of Shenzhen[ZDSYS201703031748284] ; Program for University Key Laboratory of Guangdong Province[2017KSYS008]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic

WOS:000682772901129

Web of Science

1.Graduate School of Engineering, Osaka Prefecture University
2.Faculty of Computer Science and Information Technology, University of Malaya
3.Department of Computer Science and Engineering, Southern University of Science and Technology

Naoki Masuyama,Yusuke Nojima,Chu Kiong Loo,et al. Multi-label Classification Based on Adaptive Resonance Theory[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2020:1913-1920.