The Boruta algorithm is a wrapper built around the random forest classification algorithm. It tries to capture all the important, interesting features in data with respect to an outcome variable.
I'm using boruta to select features before running random forest classifier. What I found is that the features selected is highly depend on the seeds I set. The selected features range from 5 to 8, depending on the seeds. I understand different seed might produce somehow different results. But my results vary quite a bit. Is this normal? If the result is so 'unstable', depend on the seeds, how I can justify my feature selection method?