Feature Selection in dataset containing both string and numerical values?
Feature selection algorithms assigns weights to different features based on their impact in the classification. In my best knowledge the features types does not make difference when computing different weights. I suggest to convert string features to numerical based on their ASCII codes or any other techniques. Then you can use the existing feature selection algorithm in rapid miner.
Hi I have big dataset which has both strings and numerical values
ex.
User name (str) ,  handset(str), number of requests(int), number of downloads(int) ,.......
I have around 200 such columns. 
Is there a way/algorithm which can handle both strings and integers during feature selection ?
Or how should I approach this issue.
thanks