Open Access

ARTICLE

Cross-Domain TSK Fuzzy System Based on Semi-Supervised Learning for Epilepsy Classification

by Zaihe Cheng¹, Yuwen Tao², Xiaoqing Gu³, Yizhang Jiang², Pengjiang Qian^2,*

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.

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Keywords

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