Open Access
ARTICLE
Cross-Domain TSK Fuzzy System Based on Semi-Supervised Learning for Epilepsy Classification
Zaihe Cheng1, Yuwen Tao2, Xiaoqing Gu3, Yizhang Jiang2, Pengjiang Qian2,*
1
School of Internet of Things, Wuxi Institute of Technology, Wuxi, 214121, China
2
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China
3
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, 213164, China
* Corresponding Author: Pengjiang Qian. Email:
Computer Modeling in Engineering & Sciences 2023, 137(2), 1613-1633. https://doi.org/10.32604/cmes.2023.027708
Received 10 November 2022; Accepted 07 March 2023; Issue published 26 June 2023
Abstract
Through semi-supervised learning and knowledge inheritance, a novel Takagi-Sugeno-Kang (TSK) fuzzy system
framework is proposed for epilepsy data classification in this study. The new method is based on the maximum
mean discrepancy (MMD) method and TSK fuzzy system, as a basic model for the classification of epilepsy data.
First, for medical data, the interpretability of TSK fuzzy systems can ensure that the prediction results are traceable
and safe. Second, in view of the deviation in the data distribution between the real source domain and the target
domain, MMD is used to measure the distance between dierent data distributions. The objective function is
constructed according to the MMD distance, and the distribution distance of dierent datasets is minimized to find the similar characteristics of dierent datasets. We introduce semi-supervised learning to further explore
the relationship between data. Based on the MMD method, a semi-supervised learning (SSL)-MMD method is
constructed by using pseudo-tags to realize the data distribution alignment of the same category. In addition,
the idea of knowledge dissemination is used to learn pseudo-tags as additional data features. Finally, for epilepsy
classification, the cross-domain TSK fuzzy system uses the cross-entropy function as the objective function and
adopts the back-propagation strategy to optimize the parameters. The experimental results show that the new
method can process complex epilepsy data and identify whether patients have epilepsy.
Keywords
Cite This Article
APA Style
Cheng, Z., Tao, Y., Gu, X., Jiang, Y., Qian, P. (2023). Cross-domain TSK fuzzy system based on semi-supervised learning for epilepsy classification. Computer Modeling in Engineering & Sciences, 137(2), 1613-1633. https://doi.org/10.32604/cmes.2023.027708
Vancouver Style
Cheng Z, Tao Y, Gu X, Jiang Y, Qian P. Cross-domain TSK fuzzy system based on semi-supervised learning for epilepsy classification. Comput Model Eng Sci. 2023;137(2):1613-1633 https://doi.org/10.32604/cmes.2023.027708
IEEE Style
Z. Cheng, Y. Tao, X. Gu, Y. Jiang, and P. Qian "Cross-Domain TSK Fuzzy System Based on Semi-Supervised Learning for Epilepsy Classification," Comput. Model. Eng. Sci., vol. 137, no. 2, pp. 1613-1633. 2023. https://doi.org/10.32604/cmes.2023.027708