From: Selecting critical features for data classification based on machine learning methods
Method | Accuracy | Features |
---|---|---|
SVM | 0.933 | 6 |
Boruta + SVM | 0.8866 | 4 |
RFE + SVM | 0.8895 | 4 |
RF + SVM | 0.8401 | 4 |
LDA | 0.8808 | 6 |
Boruta + LDA | 0.843 | 4 |
RFE + LDA | 0.8372 | 4 |
RF + LDA | 0.843 | 4 |
KNN | 0.8634 | 6 |
Boruta + KNN | 0.8663 | 4 |
RFE + KNN | 0.8808 | 4 |
RF + KNN | 0.8227 | 4 |
RF | 0.9331 | 6 |
Boruta + RF | 0.8792 | 4 |
RFE + RF | 0.8779 | 4 |
RF + RF | 0.9336 | 4 |