Many-objective feature selection for motor imagery EEG signals using differential evolution and support vector machine

Author(s):  
Monalisa Pal ◽  
Sanghamitra Bandyopadhyay
2013 ◽  
Vol 52 (2) ◽  
pp. 131-139 ◽  
Author(s):  
Saugat Bhattacharyya ◽  
Abhronil Sengupta ◽  
Tathagatha Chakraborti ◽  
Amit Konar ◽  
D. N. Tibarewala

2010 ◽  
Vol 27 (4) ◽  
pp. 299-310 ◽  
Author(s):  
Xinping Song ◽  
Yongsheng Ding ◽  
Jingwen Huang ◽  
Yan Ge

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Jhansi Rani Kaka ◽  
K. Satya Prasad

Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.


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