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.
Highlights
Epilepsy is a finite episode of brain dysfunction caused by abnormal discharge of cerebral neurons
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
A view-reduction principle is set out that weak views are automatically reduced by comparing the learnt weight with a fixed threshold which is automatically set according to the number of samples and the dimension of feature space
Summary
Epilepsy is a finite episode of brain dysfunction caused by abnormal discharge of cerebral neurons. Despite multi-view TSK fuzzy systems can generate promising performance and good interpretability for EEG recognition, there still exists several challenges to be addressed. In most TSK fuzzy systems, a fuzzy grid is often employed to group the input space into different subsets and generate fuzzy rules Such a grid can cause the rule-explosion problem so that the interpretability will be inevitably degraded with the increasing number of features [8]. In DVR-TSK-FS, a sampledistribution-dependent parameter is defined to control the learning of the view weight during multi-view collaborative learning in each component This parameter is user-free and set according to the feature space in advance such that the learnt weight of each view reflects the amount of effective / valuable pattern information involved in each view.
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