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