Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests
We propose novel <italic>model transfer learning</italic> methods that refine a decision forest model <inline formula><tex math notation="LaTeX"$M$/tex math><alternatives> <inline graphic xlink:href="segev ieq1 2618118.gif"/alternatives>/inline formula> learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optimal modifications of each tree structure by trying to locally expand or reduce the tree around individual nodes. The second algorithm does not modify structure, but only the parameter (thresholds) associated with decision nodes. We also propose to combine both methods by considering an ensemble that contains the union of the two forests. The proposed methods exhibit impressive experimental results over a range of problems.
IEEE Transactions on Pattern Analysis and Machine Intelligence