From: Functional discrimination of membrane proteins using machine learning techniques
Method | 5-fold cross-validation | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Sensitivity | Precision | F-Measure | Accuracy | |||||||
F1 | F2 | F3 | F1 | F2 | F3 | F1 | F2 | F3 | (%) | |
Bayesnet | 0.329 | 0.735 | 0.567 | 0.554 | 0.515 | 0.572 | 0.413 | 0.606 | 0.569 | 54.6 |
Naive Bayes | 0.202 | 0.757 | 0.575 | 0.477 | 0.512 | 0.534 | 0.284 | 0.611 | 0.554 | 51.8 |
Logistic function | 0.533 | 0.713 | 0.705 | 0.689 | 0.717 | 0.604 | 0.601 | 0.715 | 0.651 | 65. |
RBF network | 0.247 | 0.727 | 0.633 | 0.486 | 0.593 | 0.530 | 0.328 | 0.654 | 0.577 | 54.6 |
Support vector machine | 0.163 | 0.727 | 0.826 | 0.847 | 0.705 | 0.529 | 0.273 | 0.716 | 0.645 | 60.0 |
k-nearest neighbor | 0.629 | 0.705 | 0.640 | 0.634 | 0.683 | 0.651 | 0.632 | 0.694 | 0.646 | 65.6 |
Bagging meta learning | 0.553 | 0.685 | 0.737 | 0.676 | 0.733 | 0.625 | 0.608 | 0.709 | 0.676 | 66.7 |
Classification via Regression | 0.465 | 0.721 | 0.721 | 0.686 | 0.702 | 0.602 | 0.547 | 0.711 | 0.656 | 64.5 |
Decision tree J4.8 | 0.543 | 0.625 | 0.555 | 0.526 | 0.592 | 0.593 | 0.534 | 0.609 | 0.574 | 57.2 |
NBTree | 0.471 | 0.570 | 0.659 | 0.553 | 0.656 | 0.548 | 0.508 | 0.610 | 0.598 | 57.7 |
Partial decision tree | 0.520 | 0.647 | 0.623 | 0.551 | 0.645 | 0.600 | 0.535 | 0.646 | 0.612 | 60.0 |
Neural network | 0.549 | 0.701 | 0.761 | 0.695 | 0.780 | 0.622 | 0.613 | 0.739 | 0.684 | 68.1 |
Jack-knife test | 0.500 | 0.703 | 0.729 | 0.639 | 0.749 | 0.607 | 0.561 | 0.726 | 0.663 | 65.4 |
Equal data | 0.723 | 0.743 | 0.574 | 0.691 | 0.712 | 0.630 | 0.707 | 0.727 | 0.601 | 68.0 |