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Title: Epileptic EEG signals recognition using a deep view-Reduction TSK fuzzy system with high interpretability
Authors: Zhang, YP 
Li, XZ
Zhu, JQ
Wu, CY
Wu, QF
Issue Date: 2019
Source: IEEE access, 20 Sept. 2019, v. 7, p. 137344-137354
Abstract: Takagi-Sugeno-Kang (TSK) fuzzy systems are well known for their good balance between approximation accuracy and interpretability. In this paper, we propose a deep view-reduction TSK fuzzy system termed as DVR-TSK-FS in which two powerful mechanisms associating with a deep structure are developed: 1) during the multi-view learning in each component, a sample-distribution-dependent parameter is defined to control the learning of the weight of each view. The parameter is not fixed by users, it is set according to the feature space in advance such that the learnt weight of each view indeed reflects the amount of pattern information involved in each view; 2) during the iteration of DRV-TSK-FS in each component, weak views are automatically reduced by comparing the learnt weight with a fixed threshold which is also automatically set according to the number of objects and the dimension of the feature space. 3) All components are linked in a stacked way based on the stacked generalization principle such that the outputs of all previous components are augmented into the current one which can help open the manifold structure of the original feature space. DRV-TSK-FS is testified on a multi-view EEG dataset for epileptic EEG signals recognition.
Keywords: Multi-view learning
Stacked generalization principle
View reduction
TSK fuzzy systems
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2942641
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
The following publication Y. Zhang, X. Li, J. Zhu, C. Wu and Q. Wu, "Epileptic EEG Signals Recognition Using a Deep View-Reduction TSK Fuzzy System With High Interpretability," in IEEE Access, vol. 7, pp. 137344-137354, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2942641
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