I have applied the 10 fold cross-validation technique to estimate the accuracy since it is more general mainly because we can get the accuracy with a certain range of tolerance.
You can note from simulations that we get a very high classification rate for both SVM and KNN. This is mainly because we have selected the most significant features that have the highest variances. And so I have used a variance-based filter to build my classifiers. However, for fewer number of features the classification rate decreases since this leads to lower-fitting because we don't have enough features to train our classifiers