Robust and Adaptive Incremental Learning for Varying Feature Space

Real-world multiple or streaming tabular datasets, such as electronic health records from various sources and internet-of-things data generated from different devices, typically exhibit varied feature spaces depending on the datasets. Batch-mode learning with these types of datasets is often inefficient or impractical due to time constraints or privacy regulations. Therefore, an incremental-learning model capable of handling dynamically varying feature spaces, without relying on previous data, is required. To address this need, we propose a new incremental-learning method called Robust and Adaptive Incremental Learning (RAIL). RAIL comprises two core components: an incremental classifier based on naïve Bayes, and a novel adaptive feature-weighting component that utilizes feature-to-feature and feature-to-class relations. RAIL robustly handles missing and new features and adaptively assigns feature weights to improve representation capability while maintaining robustness. Based on public tabular datasets from diverse categories, we demonstrate that RAIL exhibits effective incremental-learning performance for various scenarios where the feature space regularly or arbitrarily varies. Furthermore, we validate that the proposed adaptive feature-weighting method significantly improves prediction accuracy. Additionally, we show that RAIL is more robust in preserving acquired knowledge than the existing state-of-the-art methods. Thus, our approach provides a viable incremental-learning solution for dynamic environments involving varying features.