Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological Clustering
This addresses the challenge of continual learning for machine learning systems that need to adapt to new classes over time, though it appears incremental as it builds on existing ART-based methods.
The paper tackles the problem of continual learning in supervised classification by proposing an algorithm that uses Adaptive Resonance Theory-based topological clustering independently for each class, enabling the addition of new classes without retraining. Simulation experiments demonstrated superior classification performance compared to state-of-the-art continual learning clustering-based algorithms.
This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm. The ART-based clustering algorithm is theoretically capable of continual learning, and the proposed algorithm independently applies it to each class of training data for generating classifiers. Whenever an additional training data set from a new class is given, a new ART-based clustering will be defined in a different learning space. Thanks to the above-mentioned features, the proposed algorithm realizes continual learning capability. Simulation experiments showed that the proposed algorithm has superior classification performance compared with state-of-the-art clustering-based classification algorithms capable of continual learning.