Overcoming Class Imbalance in Incremental Learning Using an Elastic Weight Consolidation-Assisted Common Encoder Approach

Engin Baysal and Cüneyt Bayılmış ()
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Engin Baysal: Computer and Information Engineering, Institute of Natural Sciences, Sakarya University, 54050 Sakarya, Türkiye
Cüneyt Bayılmış: Computer Engineering, Faculty of Computer and Information Sciences, Sakarya University, 54050 Sakarya, Türkiye

Mathematics, 2025, vol. 13, issue 11, 1-16

Abstract: Incremental learning empowers models to continuously acquire knowledge of new classes while retaining previously learned information. However, catastrophic forgetting and class imbalance often impede this process, especially when new classes are introduced sequentially. We propose a hybrid method that integrates Elastic Weight Consolidation (EWC) with a shared encoder architecture to overcome these obstacles. This approach provides robust feature extraction, while EWC safeguards vital parameters and preserves prior knowledge. Moreover, task-specific output layers enable flexible adaptation to new classes. We evaluated our method using the CICIoT2023 dataset, a class-incremental IoT anomaly detection benchmark. Our results demonstrated a 15.3% improvement in the macro F1-score and a 1.28% increase in overall accuracy compared to a baseline model that did not incorporate EWC, with particular advantages for underrepresented classes. These findings underscore the effectiveness of the EWC-assisted shared encoder framework for class-imbalanced incremental learning in streaming environments.

Keywords: continual learning; incremental learning; class imbalance; catastrophic forgetting; common encoder (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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