TY - GEN
T1 - Joint distribution adaptation based TSK Fuzzy logic system for epileptic EEG signal identification
AU - Feng, Hao
AU - Peng, Yaxin
AU - Zhang, Guixu
AU - Shen, Chaomin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/17
Y1 - 2017/1/17
N2 - Transfer learning based method, which utilizes plenty labeled data in the source domain to build an accuracy classifier for the target domain, serves as an effective means in the epileptic detection by using electroencephalogram (EEG) signals. Among existing approaches, Fuzzy logic system (FLS) based on transductive transfer learning is an efficient method due to its superior interpretability and strong learning abilities. However, this kind of method cannot simultaneously reduce the differences in both marginal distributions and conditional distributions between the training and test datasets of EEG signals. To overcome this problem, in this paper, we construct a Takagi-Sugeno-Kang (TSK) FLS based on the joint distribution adaptation (JDA), which refers to TSK-JDA-FLS. It aims to match both marginal and conditional distributions, and we extend the algorithm to perform a multi-class classification for identifying epileptic EEG signals. Extensive experiments verify that TSK-JDA-FLS significantly outperforms competitive non-transfer learning and transfer learning methods in the epileptic EEG datasets.
AB - Transfer learning based method, which utilizes plenty labeled data in the source domain to build an accuracy classifier for the target domain, serves as an effective means in the epileptic detection by using electroencephalogram (EEG) signals. Among existing approaches, Fuzzy logic system (FLS) based on transductive transfer learning is an efficient method due to its superior interpretability and strong learning abilities. However, this kind of method cannot simultaneously reduce the differences in both marginal distributions and conditional distributions between the training and test datasets of EEG signals. To overcome this problem, in this paper, we construct a Takagi-Sugeno-Kang (TSK) FLS based on the joint distribution adaptation (JDA), which refers to TSK-JDA-FLS. It aims to match both marginal and conditional distributions, and we extend the algorithm to perform a multi-class classification for identifying epileptic EEG signals. Extensive experiments verify that TSK-JDA-FLS significantly outperforms competitive non-transfer learning and transfer learning methods in the epileptic EEG datasets.
KW - Distribution adaptation
KW - Epilepsy EEG signals
KW - TSK fuzzy logic system
KW - Transfer learning
UR - https://www.scopus.com/pages/publications/85013301892
U2 - 10.1109/BIBM.2016.7822543
DO - 10.1109/BIBM.2016.7822543
M3 - 会议稿件
AN - SCOPUS:85013301892
T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
SP - 340
EP - 345
BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
A2 - Burrage, Kevin
A2 - Zhu, Qian
A2 - Liu, Yunlong
A2 - Tian, Tianhai
A2 - Wang, Yadong
A2 - Hu, Xiaohua Tony
A2 - Jiang, Qinghua
A2 - Song, Jiangning
A2 - Morishita, Shinichi
A2 - Burrage, Kevin
A2 - Wang, Guohua
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Y2 - 15 December 2016 through 18 December 2016
ER -