In the next experiment we test whether the combination of feature sets changes our classification results. To balance the size of the feature sets we use Principal Component Analysis (PCA) and construct 100 features from the 3000 mfw and 1000 4gram features each. As shown in figure 5 some feature sets improve when combined (e.g. 4gram100 and lda100) and for others (e.g. lda100 and network) performance decreases. But classification results vary greatly in this setting as signaled by standard deviation bars so these differences should not be overrated.