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Detail
Journal Article
Robust and Adaptive Incremental Learning for Varying Feature Space
- Authors
- Cheol Ho Kim, Jung-Hoon Lee, Hawon Shin, Ock Kee Baek
- Issue Date
- 2024-05
- Citation
- IEEE Access, v.12, pp.64177-64192
- ISSN
- 2169-3536
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Language
- English
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- Adaptive incremental learning, Batch-mode, Dynamic Environment, Feature space, Internet of thing(IoT), Learning methods, Learning model, Learning performance, Prediction accuracy, Privacy regulations, Real-world
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
(CC BY NC ND)
