I want build a classifier that classifies sentences into two categories, and for that I have a training set of 1000 labeled sentences. My features consist of a list of about 8000 words, and for each sentence I measure the word frequency (feature divided by total words in the sentence).
I've been reading up on the Mahalanobis distance, but I haven't yet managed to understand how I could apply it to my problem. How can I use it to select the features that work best for distinguishing between the two categories?
Thank you!