Fig. 1. Description of ActiveSVM feature selection.

At the nth step, an n-D SVM using only already-selected genes is trained to select a certain number of misclassified cells, which is the cell selection step. In the gene selection step, the least classifiable cells are taken as the training set. Based on this training set, N – n (n + 1)-D SVMs are trained, where n dimensions are the genes already selected and the last dimension is one of the previously unselected candidate genes. We would then obtain N – n weights $w^{\prime }$ corresponding to N – n unselected genes as well as N – n margin rotation angles θ between every $w^{\prime }$ and the original weight w of the n-D SVM. The gene with the maximum rotation of margin is selected for the next round.