Figure 3 shows the feature contribution which is evaluated by NECGT and CoFS.
Figure 4(a) shows that NECGT performs much better than CoFS except the third time iteration.
In contrast, we can see that NECGT appears completely normal in the experiment.
For further comparison between NECGT and CoFS, we test the classification performance and running time on different datasets besides Lymphography and Wpbc.
It can be concluded that NECGT is more efficient by avoiding discretization.
NECGT indeed enhances the ability of classification of the attributes subsets.
(2) Although NECGT takes quite some time consequentially, the traditional methods are improved by NECGT.