Feature selection methods |          Classifier algorithm |
---|---|
  A-SVM; B-WSVM; C-MAPLSC; D-Naïve Bayes; E-kNN | |
Unused | B > A(P = 3.2 × 10-7), A > C(P = 2.3 × 10-6), A > D(P = 1.8 × 10-19) |
A > E(P = 1.5 × 10-61), B > C(P = 8.5 × 10-21),B > D(P = 1.5 × 10-40) | |
B > E(P = 2.7 × 10-90), C > D(P = 3.2 × 10-6), C > E(P = 2.2 × 10-36) | |
D > E(P = 4.5 × 10-17) | |
SVM-RFE | A > B(P = 1.3 × 10-16), A > C(P = 3.5 × 10-26), A > D(P = 7.0 × 10-14) |
A > E(P = 5.1 × 10-50), B > C(P = 3.3 × 10-3), B > E(P = 2.6 × 10-13) | |
D > C(P = 1.4 × 10-4), C > E(P = 6.2 × 10-6), D > E(P = 1.4 × 10-16), | |
mRMR | B > A(P = 5.1 × 10-3), A > C(P = 1.2 × 10-18), A > E(P = 6.0 × 10-28) |
B > C(P = 1.5 × 10-28), B > D(P = 3.1 × 10-3), B > E(P = 6.2 × 10-40) | |
D > C(P = 5.7 × 10-17), C > E(P = 2.5 × 10-2),D > E(P = 1.5 × 10-25) | |
IG | A > C(P = 5.9 × 10-11), A > E(P = 6.2 × 10-19), B > C(P = 1.3 × 10-15) |
B > D(P = 1.9 × 10-2), B > E(P = 5.0 × 10-25), D > C(P = 3.1 × 10-9) | |
C > E(P = 9.3 × 10-3), D > E(P = 1.7 × 10-16) | |
Total Rank | WSVM(11) > SVM(9) > Naïve Bayes(1) > MAPLSC(−6) > kNN(−16) |