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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Vydané v:	IEEE Conference on Evolving and Adaptive Intelligent Systems (Online) s. 1 - 10
Hlavní autori:	Leite, Daniel, Silva, Alisson, Casalino, Gabriella, Sharma, Arnab, Fortunato, Danielle, Ngomo, Axel-Cyrille
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 23.05.2024
Predmet:	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
On-line prístup:	Získať plný text
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