Masuyama, Naoki and Nojima, Yusuke and Loo, Chu Kiong and Ishibuchi, Hisao (2023) Multi-label classification via adaptive resonance theory-based clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 (7). pp. 8696-8712. ISSN 0162-8828, DOI https://doi.org/10.1109/TPAMI.2022.3230414.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT), 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 (NSFC) 61876075, Guangdong Provincial Key Laboratory 2020B121201001, Shenzhen Science and Technology Program KQTD2016112514355531 |
| Uncontrolled Keywords: | Multi-label classification; Continual learning; Clustering; Adaptive resonance theory; Correntropy |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 08 Nov 2025 10:56 |
| Last Modified: | 08 Nov 2025 10:56 |
| URI: | http://eprints.um.edu.my/id/eprint/49741 |
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