@Article{cmes.2023.027708,
AUTHOR = {Zaihe Cheng, Yuwen Tao, Xiaoqing Gu, Yizhang Jiang, Pengjiang Qian},
TITLE = {Cross-Domain TSK Fuzzy System Based on Semi-Supervised Learning for Epilepsy Classification},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {2},
PAGES = {1613--1633},
URL = {http://www.techscience.com/CMES/v137n2/53368},
ISSN = {1526-1506},
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.
},
DOI = {10.32604/cmes.2023.027708}
}