One of the advantage of using a features reduction/sub feature selection is to avoid a high dimentionality.
The most known method is the Forward selection, where it finds the best combinations of features among others, iteratively. Now please have a look at this picture retrieved from a ppt slide on the internet.
The second row of the table shows that the 11 features are the features selected/reduced from initial 20 features. My question is that, the 11 features are still considered as a high-dimensional features set, indeed one can't just subtitute it over a KNN function. Now I'm confused as to how he/she then adapt it to the KNN classifier for the 11 features?