I am doing a machine learning project using WEKA. It is a supervised classification and in my basic experiments, I achieved very poor level of accuracy. Then my intention was to do a feature selection, but then I heard about PCA.
In feature selection, what we do is we consider a subset of attributes which has the greatest impact towards our targeted classification.(If I am correct.)
In PCA, as far as I know, what we do is we generate a smaller amount of artificial set of attributes that will account for our target.(please correct me if I am wrong)
But I cannot understand what is the exact difference between these two. Which one is better? Does it depend on the particular study someone is doing?
And also, what about a combination of above two methodologies? (A PCA after a feature selection). Does it make any sense?