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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2103.01511 |
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| _version_ | 1866913527893065728 |
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| author | Masuyama, Naoki Nojima, Yusuke Loo, Chu Kiong Ishibuchi, Hisao |
| author_facet | Masuyama, Naoki Nojima, Yusuke Loo, Chu Kiong Ishibuchi, Hisao |
| contents | This paper 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_01511 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Multi-label Classification via Adaptive Resonance Theory-based Clustering Masuyama, Naoki Nojima, Yusuke Loo, Chu Kiong Ishibuchi, Hisao Machine Learning This paper 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. |
| title | Multi-label Classification via Adaptive Resonance Theory-based Clustering |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2103.01511 |