  Active learning is widely used to reduce labeling effort and training time by
repeatedly querying only the most beneficial samples from unlabeled data. In
real-world problems where data cannot be stored indefinitely due to limited
storage or privacy issues, the query selection and the model update should be
performed as soon as a new data sample is observed. Various online active
learning methods have been studied to deal with these challenges; however,
there are difficulties in selecting representative query samples and updating
the model efficiently without forgetting. In this study, we propose Message
Passing Adaptive Resonance Theory (MPART) that learns the distribution and
topology of input data online. Through message passing on the topological
graph, MPART actively queries informative and representative samples, and
continuously improves the classification performance using both labeled and
unlabeled data. We evaluate our model in stream-based selective sampling
scenarios with comparable query selection strategies, showing that MPART
significantly outperforms competitive models.
