Feature selection using binary simulated Kalman filter for peak classification of EEG signals
B Muhammad, MFM Jusof, MI Shapiai… - 2018 8th …, 2018 - ieeexplore.ieee.org
2018 8th International Conference on Intelligent Systems …, 2018•ieeexplore.ieee.org
Previously, an angle modulated simulated Kalman filter (AMSKF) algorithm has been
implemented for feature selection in peak classification of electroencephalogram (EEG)
signals. The AMSKF is an extension of simulated Kalman filter (SKF) algorithm for
combinatorial optimization problems. In this paper, another extension of SKF algorithm,
which is called binary SKF (BSKF) algorithm, is applied for the same feature selection
problem. It is found that the BSKF algorithm performed slightly better than the AMSKF …
implemented for feature selection in peak classification of electroencephalogram (EEG)
signals. The AMSKF is an extension of simulated Kalman filter (SKF) algorithm for
combinatorial optimization problems. In this paper, another extension of SKF algorithm,
which is called binary SKF (BSKF) algorithm, is applied for the same feature selection
problem. It is found that the BSKF algorithm performed slightly better than the AMSKF …
Previously, an angle modulated simulated Kalman filter (AMSKF) algorithm has been implemented for feature selection in peak classification of electroencephalogram (EEG) signals. The AMSKF is an extension of simulated Kalman filter (SKF) algorithm for combinatorial optimization problems. In this paper, another extension of SKF algorithm, which is called binary SKF (BSKF) algorithm, is applied for the same feature selection problem. It is found that the BSKF algorithm performed slightly better than the AMSKF algorithm.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果