Figure 1.

SVM Framework. This figure shows the data mining scheme for making TF classifiers. 100 classifiers are constructed for each TF, each using a different random sub-sample of the negative set. A classifier built on the training set is evaluated using cross-validation (center, gray box). This will usually be leave-one-out cross-validation, except for classifiers with large training sets where 5-fold cross-validation is used and repeated 10 times. For every cross-validation split, the top 1750 features are selected using SVM-RFE and the classifier is trained and finally used to classify the test set (left out sample). This process is repeated 100 times, and the accuracy for the procedure is the average of the 100 cross-validation accuracies.