4:40 PM - 5:00 PM
[1N2-05] Quantification of contribution of features to EEG classification using the efficient estimation of Shapley Value based on a hierarchy of EEG features
Keywords:Machine Learning, Shapley Value, EEG
Understanding how black-box classifiers predict is important in many applications, especially in medical diagnosis systems.
We propose a effective method to quantify contribution of features to EEG classification using the efficient estimation of Shapley Sampling Value (SSV).
EEG data have hierarchical features: an electrode signal, signals in various frequency-bands, and amplitude and phase. If contribution of a feature at a higher level (e.g., a signal of an electrode) is very small, contribution of features at the lower levels of the feature (e.g., signals of frequency-bands of the electrode) should be also small. The method prunes such features at lower levels to reduce computational complexity.
We verified the usability of the method in two datasets for EEG classification.
The result showed the method could reduce computational complexity of SSV by one third, while maintaining high accuracy of the conventional SSV.
We propose a effective method to quantify contribution of features to EEG classification using the efficient estimation of Shapley Sampling Value (SSV).
EEG data have hierarchical features: an electrode signal, signals in various frequency-bands, and amplitude and phase. If contribution of a feature at a higher level (e.g., a signal of an electrode) is very small, contribution of features at the lower levels of the feature (e.g., signals of frequency-bands of the electrode) should be also small. The method prunes such features at lower levels to reduce computational complexity.
We verified the usability of the method in two datasets for EEG classification.
The result showed the method could reduce computational complexity of SSV by one third, while maintaining high accuracy of the conventional SSV.