Multi-label Classification via Adaptive Resonance Theory-based Clustering

• URL: http://arxiv.org/abs/2103.01511v2
• Date: Wed, 3 Mar 2021 03:09:52 GMT
• Title: Multi-label Classification via Adaptive Resonance Theory-based Clustering
• Authors: Naoki Masuyama, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi
• Abstract summary: The 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. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms.
• Score: 9.58897929546191
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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