Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition

García-Salinas, Jesús S.; Torres-García, Alejandro A.; Reyes-García, Carlos A.; Villaseñor-Pineda, Luis

doi:10.1016/j.bspc.2022.104433

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…The idea of incremental learning is a simulation of humans' learning ability and is a solution to some real-life problems [16]. Incremental learning is often used in image classification [17], target detection [18], and speech recognition [19]. In the field of network intrusion detection, not only is the volume of intrusion data large but also the categories are constantly updated, resulting in data that cannot be collected completely all at once.…”

Section: Incremental Learningmentioning

confidence: 99%

SOINN Intrusion Detection Model Based on Three-Way Attribute Reduction

Ren,

Liu,

Huang

et al. 2023

Electronics

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With a large number of intrusion detection datasets and high feature dimensionality, the emergent nature of new attack types makes it impossible to collect network traffic data all at once. The modified three-way attribute reduction method is combined with a Self-Organizing Incremental learning Neural Network (SOINN) algorithm to propose a self-organizing incremental neural network intrusion detection model based on three-way attribute reduction. Attribute importance is used to perform attribute reduction, and the data after attribute reduction are fed into a self-organized incremental learning neural network algorithm, which generalizes the topology of the original data through self-organized competitive learning. When the streaming data are transferred into the model, the inter-class insertion or node fusion operation is performed by comparing the inter-node distance and similarity threshold to achieve incremental learning of the model streaming data. The inter-node distance value is introduced into the weight update formulation to replace the traditional learning rate and to optimize the topological structure adjustment operation. The experimental results show that T-SOINN achieves high precision and recall when processing intrusion detection data.

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