EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams

arXiv:2402.17792v1 Announce Type: cross
Abstract: We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robustness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to capture drifts. The classifier evolves from scratch, incorporates new classes on the fly, and performs local incremental feature weighting. As an application, we focus on the classification of …

abstract algorithm application arxiv classification classifiers coverage cs.ai cs.lg cs.ne data data streams eeg eess.sp incremental network neural network noise robustness type weakly-supervised