JSAI2018

Presentation information

Oral presentation

General Session » [General Session] 2. Machine Learning

[1N2] [General Session] 2. Machine Learning

Tue. Jun 5, 2018 3:20 PM - 5:00 PM Room N (2F Sakurajima)

座長:岡本 昌之(トヨタ自動車株式会社)

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

〇Kazuki Tachikawa1, Yuji Kawai1, Jihoon Park1, Minoru Asada1 (1. OsakaUniversity)

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.