Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning
This work addresses the problem of efficient online active learning for real-world scenarios where data cannot be stored indefinitely, which is relevant for practitioners dealing with data streams and limited resources.
This paper introduces Message Passing Adaptive Resonance Theory (MPART), an online active semi-supervised learning method. MPART learns data distribution and topology online, actively queries informative samples, and continuously improves classification performance using both labeled and unlabeled data, significantly outperforming competitive models in stream-based selective sampling.
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.