Multi-Label Classification via Adaptive Resonance Theory-Based Clustering

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

2022

10.1109/TPAMI.2022.3230414

IEEE Transactions on Pattern Analysis and Machine Intelligence   影响因子和分区

1939-3539

1939-3539

This article proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning.

Multi-label Classification Continual Learning Clustering Adaptive Resonance Theory Correntropy

SCI ; EI

英语

其他

Japan Society for the Promotion of Science (JSPS) KAKENHI[JP22K12199] ; Universiti Malaya Impact-oriented Interdisciplinary Research Grant Programme[IIRG002C-19HWB] ; Universiti Malaya Covid-19 Related Special Research under Grant (UMCSRG) from University of Malaya[CSRG008-2020ST] ; National Natural Science Foundation of China[61876075] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Stable Support Plan Program of Shenzhen Natural Science Fund[20200925174447003] ; Shenzhen Science and Technology Program[KQTD2016112514355531]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic

WOS:001004665900050

IEEE COMPUTER SOC

20230313393601

Classification (of information) ; Clustering algorithms ; Computation theory ; Learning algorithms ; Learning systems ; Machine learning

Information Theory and Signal Processing:716.1 ; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Artificial Intelligence:723.4 ; Machine Learning:723.4.2 ; Information Sources and Analysis:903.1 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4

ENGINEERING

IEEE

1.Graduate School of Informatics, Osaka Metropolitan University, Sakai-Shi, Osaka, Japan
2.Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
3.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Naoki Masuyama,Yusuke Nojima,Chu Kiong Loo,et al. Multi-Label Classification via Adaptive Resonance Theory-Based Clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2022,PP(99):1-18.

Naoki Masuyama,Yusuke Nojima,Chu Kiong Loo,&Hisao Ishibuchi.(2022).Multi-Label Classification via Adaptive Resonance Theory-Based Clustering.IEEE Transactions on Pattern Analysis and Machine Intelligence,PP(99),1-18.

Naoki Masuyama,et al."Multi-Label Classification via Adaptive Resonance Theory-Based Clustering".IEEE Transactions on Pattern Analysis and Machine Intelligence PP.99(2022):1-18.