I was attempting to analyze the Wisconsin Breast Cancer Diagnostic dataset. Have a couple of questions / doubts.
Per the attached paper, the performance metrics were worse after dimensionality reduction. Why did that happen? Is that because some information was lost when just 1 feature was selected?
So in such cases is it preferred to work with the original number of features? (30 in this case).
I read that KNN suffers from the curse of dimensionality in the presence of higher dimensions. However, in this case we can see that at K = 5 with 30 features, the accuracy is still 93% (which is not bad). So is it fine to use KNN in case of high number of features like this case?