Feature selection via decision tree surrogate splits
Clayton Springer, W. Philip Kegelmeyer · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008
CARTpsilas ldquovariable rankingrdquo provides a quick estimate of the importance of an individual feature in a decision tree, and it is based on surrogate splits. We extend this estimate to arbitrary subsets. We have applied our estimate (called ldquodIrdquo) to three datasets. The performance of dI as an importance estimate is very dependent on the underlying performance of the tree used to generate the surrogate splits.