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

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...

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Bibliographic Details
Published in:	IEEE Conference on Evolving and Adaptive Intelligent Systems (Online) pp. 1 - 10
Main Authors:	Leite, Daniel, Silva, Alisson, Casalino, Gabriella, Sharma, Arnab, Fortunato, Danielle, Ngomo, Axel-Cyrille
Format:	Conference Proceeding
Language:	English
Published:	IEEE 23.05.2024
Subjects:	Accuracy Adaptation models Classification algorithms Cognitive science Data models Decision making Electroencephalography Emotion recognition Facial features Feature extraction Fourier transforms Games Granular computing Learning systems Machine learning Neural networks Pattern recognition Physiology Spatiotemporal phenomena Time-frequency analysis
ISSN:	2473-4691
Online Access:	Get full text
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